Journal articles on the topic 'Image retrieval'
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1
LEE, SUH-YIN, and MAN-KWAN SHAN. "ACCESS METHODS OF IMAGE DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 27–44. http://dx.doi.org/10.1142/s0218001490000034.
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The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve images in an image database system. The data structure called 2-D string, proposed by Chang et al., is adopted to represent the symbolic pictures. When there are a large number of images in the image database and each image contains many objects, the processing time for image retrievals is tremendous. It is essential to develop efficient access methods for these retrievals. In this paper, the efficient methods for retrieval by objects, retrieval by pairwise spatial relationships and retrieval by subpicture are proposed. All the methods are based on the superimposed coding technique.
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Shiral, J. V., Munmun Burman, Apurva Bhadbhade, Dhanashree Patil, Kajal Motghare, and Neha Wanjari. "Retrieval of Images Using SVM." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (March 31, 2015): 106–11. http://dx.doi.org/10.53555/nncse.v2i3.500.
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Image retrieval is a technique which is used to search and retrieve images from a large database of digital images. Content-based image retrieval (CBIR) is a technique which allows searching images from large scale image database based on contents as needed by user.This paper introduces a technique to retrieve images by classifying it on the basis of the features and characteristics it contains using Support Vector Machine (SVM). The dataset of images is created which is used for feature matching purpose by SVM to find similar images from the database and based on user requirements images are retrieved.
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Mustikasari, Metty, and Sarifuddin Madenda. "Performance Analysis of Color based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 4 (January 20, 2014): 3373–81. http://dx.doi.org/10.24297/ijct.v12i4.7058.
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Recently Content based image retrieval (CBIR) is an active research. This paper proposes a technique to retrieve images based on color feature and evaluate the retrieval system performance. In this retrieval system Euclidean distance and City block distance are used to measure similarity of images. This algorithm is tested by using Corel image database which is provided by James Wang. The performance of retrieval system is measured in terms of its recall and precision. The effectiveness of retrieval system is also measured based on Average Rank (AVRR) of all relevant retrieves images and Ideal Average Rank of relevant images (IAVRR). The experimental results show that city block has achieved higher retrieval performance than Euclidean distance.
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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (December 1, 2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.
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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.
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Abubacker, K. A. Shaheer, J. Sutha, and K. A. Shahul Hameed. "A simple multi-feature based stereoscopic medical image retrieval system." Polish Journal of Medical Physics and Engineering 25, no. 2 (June 1, 2019): 127–30. http://dx.doi.org/10.2478/pjmpe-2019-0017.
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Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.
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Li, Quan. "A Partitioning Image Retrieval Method Based on Regional Division and Polymerization." Applied Mechanics and Materials 347-350 (August 2013): 2218–22. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2218.
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in order to further improve the retrieval accuracy of the image retrieval system based on shape feature. A partitioning image retrieval method based on regional division and polymerization is proposed in this paper. Firstly, an image is segmented by the regional division and polymerization method. Secondly, the shape and spatial features of different objects in the image are extracted by the invariant moment. Finally, images are retrieved by calculating the similarity of images, and five different types of images are tested by group. The experimental results show that it is more accurate for the algorithm to retrieve the users interested images.
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Li, Zhongjian, Jun Xiang, Lei Wang, Ning Zhang, Ruru Pan, and Weidong Gao. "Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm." Fibres and Textiles in Eastern Europe 27, no. 5(137) (October 31, 2019): 60–69. http://dx.doi.org/10.5604/01.3001.0013.2900.
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Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.
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Xie, Dan, and Chao Yin. "Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology." PeerJ Computer Science 9 (April 18, 2023): e1330. http://dx.doi.org/10.7717/peerj-cs.1330.
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Image retrieval technology has emerged as a popular research area of China’s development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network’s capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system’s picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.
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Gao, Fei. "Rapid Feature Retrieval Method in Large-Scale Image Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (November 20, 2018): 1088–92. http://dx.doi.org/10.20965/jaciii.2018.p1088.
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The retrieval of features in a large-scale image database can improve the degree of visualization of images. The conventional method of feature-retrieval is a time-consuming process because it retrieves by searching the keywords. In this paper, a rapid feature retrieval method based on granular computing is proposed for use in a large-scale image database. In this method, we first collect and process the images from the database. Next, we construct a binary tree to realize the multi-class classification of the image features and complete the feature retrieval using support vector machines. The experimental results demonstrate that the proposed method can effectively retrieve the features in the large-scale image database. The effectiveness of retrieval can reach more than 95%.
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Gupta, Rajeev, and Virender Singh. "COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE." International Journal of Students' Research in Technology & Management 8, no. 1 (January 26, 2020): 01–10. http://dx.doi.org/10.18510/ijsrtm.2020.811.
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Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace.
Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time.
Main Findings: This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query.
Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace.
Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.
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R, Dhaya. "Analysis of Adaptive Image Retrieval by Transition Kalman Filter Approach based on Intensity Parameter." Journal of Innovative Image Processing 3, no. 1 (March 9, 2021): 7–20. http://dx.doi.org/10.36548/jiip.2021.1.002.
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The information changes in image pixel of retrieved records is very common in image process. The image content extraction is containing many parameters to reconstruct the image again for access the information. The intensity level, edge parameters are important parameter to reconstruct the image. The filtering techniques used to retrieve the image from query images. In this research article, the adaptive function kalman filter function performs for image retrieval to get better accuracy and high reliable compared to previous existing method includes Content Based Image Retrieval (CBIR). The kalman filter is incorporated with adaptive feature extraction for transition framework in the fine tuning of kalman gain. The feature vector database analysis provides transparent to choose the images in retrieval function from query images dataset for higher retrieval rate. The virtual connection is activated once in single process for improving reliability of the practice. Besides, this research article encompasses the adaptive updating prediction function in the estimation process. Our proposed framework construct with adaptive state transition Kalman filtering technique to improve retrieval rate. Finally, we achieved 96.2% of retrieval rate in the image retrieval process. We compare the performance measure such as accuracy, reliability and computation time of the process with existing methods.
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Anandh, A., K. Mala, and R. Suresh Babu. "Combined global and local semantic feature–based image retrieval analysis with interactive feedback." Measurement and Control 53, no. 1-2 (December 12, 2019): 3–17. http://dx.doi.org/10.1177/0020294018824122.
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Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.
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Rimiru, Richard M., Judy Gateri, and Micheal W. Kimwele. "GaborNet: investigating the importance of color space, scale and orientation for image classification." PeerJ Computer Science 8 (February 25, 2022): e890. http://dx.doi.org/10.7717/peerj-cs.890.
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Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval.
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NAIR, PRIYA C., and Dr T. Jebarajan. "Enhanced LTrP For Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 9 (March 17, 2014): 3912–20. http://dx.doi.org/10.24297/ijct.v12i9.2832.
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Local Tetra Pattern (LTrP) is an image retrieval and indexing algorithm for content based image retrieval (CBIR) which made a significant improvement in the precision and recall rates of the retrieved images. Enhanced LTrP for Image Retrieval (ELIR) proposes a novel method of image retrieval by adding additional features to LTrP together with the features of coarseness, contrast, directionality and busyness. The experimental results show that precision and recall of image retrieval improved from that of using LTrP alone.
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Pham, Nam, Jong-Weon Lee, Goo-Rak Kwon, and Chun-Su Park. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry 11, no. 1 (January 14, 2019): 83. http://dx.doi.org/10.3390/sym11010083.
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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.
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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.
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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.
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Mamatha, Ch, Dr V. Anandam, Priyadarshini Chatterjee, and Hepshiba Vijaya Kumari. "Attribute Based Image Retrieval and Segmentation using On-tological Approaches." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.6.20440.
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Content based image retrieval is gaining more and more importance as it is an apt approach to retrieve an image. The image is retrieved based on certain texture. Ontology is a branch of Meta Physics that helps in analyzing an input image based on certain textures. Ontology helps to retrieve an image based on its properties. Ontology describes a domain. With that domain, we can proceed further to understand the relation between the features present in the domain. There are biological-ontologies to analyze biological outcomes. The field of information technology can be combined with biological ontology to study the results of different biological effects. With the systematic concept of ontology that includes rules, classes, relations etc we can understand an image better that eventually helps in accurate image retrieval. Ontology can be generic or domain specific. In this paper we will be using domain specific ontology used to analyze the features of digital images along with image segmentation to retrieve an image. We will be testing our proposed system using the colored images of mammals. In case of image segmentation we will using the general techniques already existing.
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Su, Ching Hung, Huang Sen Chiu, Mohd Helmy A. Wahab, Tsai Ming Hsiehb, You Chiuan Li, and Jhao Hong Lin. "Images Retrieval Based on Integrated Features." Applied Mechanics and Materials 543-547 (March 2014): 2292–95. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2292.
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We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.
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Su, Ching Hung, Chiun Hsiun Lin, Hsuan Shu Huang, and Kuo Chin Fan. "Using Color Sequences for Cartoon Image Retrieval." Advanced Materials Research 433-440 (January 2012): 5308–12. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5308.
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We propose a practical cartoon image retrieval scheme to retrieve cartoon images efficiently. The proposed scheme transfers each cartoon image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the cartoon images, namely color sequences comparison. We succeed in transferring the cartoon image retrieval problem to sequences comparison. Thus the computational complexity is decreased obviously. Our system keeps both advantages of the content based cartoon image retrieval system (similarity-based retrieval) and a text based cartoon image retrieval system (very rapid and mature).
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Naveen, Mr Kommu. "A Review on Content Based Image Retrieval System Features derived by Deep Learning Models." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 42–57. http://dx.doi.org/10.22214/ijraset.2021.39172.
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Abstract: In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the “semantic gap”. In this paper, we propose to use features derived from pre-trained network models from a deep- learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases. Keywords Content Based Image Retrieval Feature Selection Deep Learning Pre-trained Network Models Pre-clustering
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Reddy. K*, Srinivasa, and Jaya T. "Medical Image Retrieval using Two Dimensional PCA." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (February 28, 2020): 1852–56. http://dx.doi.org/10.35940/ijitee.d1152.018520.
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Medical image analysis will be used to develop image retrieval system to provide access to image databases using extracted features. Content Based Image Retrieval (CBIR) is used for retrieving similar images from image databases. During the last few years, medical images are grown and used for medical image analysis. Here, we are proposed that medical image retrieval using two dimensional Principal Component Analysis (2DPCA). For extracting medical image features, 2DPCA has advantageous that evaluates accurate covariance matrix easily as much smaller and also requires less time for finding Eigen vectors. Medical image reconstruction is performed with increased values of 2DPCA and observed from results that reconstruction accuracy improves with increase of principal component values. Retrieval is performed for transformed image space by calculating the Euclidean Distance(ED) between 2DPCA values of unknown images with database images. Minimum distance classifier is used for retrieval, which is simple classifier. Simulation results are reported by considering different medical images and showed that simulation results provide increased retrieval accuracy. Further, Segmentation of retrieved medical images is obtained using k-means clustering algorithm.
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Varma, Ankitha, and Dr Kamalpreet Kaur. "Survey on content based image retrieval." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 471. http://dx.doi.org/10.14419/ijet.v7i4.5.21136.
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Now-a-days, because of the advancement in the digital technology and the use of internet, a huge amount of digital data is available in the form of medical images, remote sensing, digital museums, geographical information, etc. This has lead to the need of accurate and efficient techniques for the search and retrieval of relevant images from such voluminous datasets. Content based image retrieval (CBIR) is one such approach which is increasingly being used to search and retrieve query image from the databases. CBIR combines features of color, texture as well as shape which ease out the process of extracting desired information from the retrieved images. This paper pre- sents a systematic and a detailed review of the CBIR method along with the different databases and evaluation parameters used for the analysis. An attempt has been made to include an exhaustive literature survey of the various CBIR approaches.
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Cui, Chenhao, and Zhoujun Li. "Prompt-Enhanced Generation for Multimodal Open Question Answering." Electronics 13, no. 8 (April 10, 2024): 1434. http://dx.doi.org/10.3390/electronics13081434.
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Multimodal open question answering involves retrieving relevant information from both images and their corresponding texts given a question and then generating the answer. The quality of the generated answer heavily depends on the quality of the retrieved image–text pairs. Existing methods encode and retrieve images and texts, inputting the retrieved results into a language model to generate answers. These methods overlook the semantic alignment of image–text pairs within the information source, which affects the encoding and retrieval performance. Furthermore, these methods are highly dependent on retrieval performance, and poor retrieval quality can lead to poor generation performance. To address these issues, we propose a prompt-enhanced generation model, PEG, which includes generating supplementary descriptions for images to provide ample material for image–text alignment while also utilizing vision–language joint encoding to improve encoding effects and thereby enhance retrieval performance. Contrastive learning is used to enhance the model’s ability to discriminate between relevant and irrelevant information sources. Moreover, we further explore the knowledge within pre-trained model parameters through prefix-tuning to generate background knowledge relevant to the questions, offering additional input for answer generation and reducing the model’s dependency on retrieval performance. Experiments conducted on the WebQA and MultimodalQA datasets demonstrate that our model outperforms other baseline models in retrieval and generation performance.
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Sarath, Devika, and M. Sucharitha. "A Study on Image Retrieval Based on Tetrolet Transform." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 321. http://dx.doi.org/10.14419/ijet.v7i3.27.17964.
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Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.
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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (September 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.
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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.
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Ahmed, Ali M., Saadi M. Saadi, and Karrar Neamah Hussein. "Image Retrieval Based on Chain Code Algorithm Using Color and Texture Features." Journal of Kufa for Mathematics and Computer 4, no. 2 (June 30, 2017): 18–26. http://dx.doi.org/10.31642/jokmc/2018/040203.
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The rapid growth of image retrieval has provided an efficient Content-Based Image Retrieval CBIR system to retrieve image accurately. In this paper, a precise retrieval result by exploiting color, texture and shape features is proposed. First, extract the features by color moment and (Hue, Saturation, Value HSV color space as a color feature, and then get the co-occurrence matrix as well as Discrete Wavelet Transform DWT for a texture feature. Chain codes algorithm, specifically chain code histogram, is then applied to obtain the codes of the shape feature. Second, collect all these features and store it in the database, where each record represents one image of the dataset. Similarity process is executed to find the images that are more similar to the query image, retrieved images ranked. The dataset applied in this study is WANG that includes 10 classes with each class containing 100 images. Experimental results have revealed that the proposed method outperformed the previous studies with an average of 0.824 in term of precision
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Su, Ching Hun, Huang Sen Chiu, and Tsai Ming Hsieh. "Content Based Images Retrieval Based on HSV Color Space and GLCM." Applied Mechanics and Materials 644-650 (September 2014): 4287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4287.
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We propose a practical image retrieval scheme to retrieve images efficiently. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Gray Level Co-occurrence matrix to compare the images of database. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues of both the content based image retrieval system and a text based image retrieval system. Experimental results reveal that proposed scheme is better than the conventional methodologies.
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Shirsath, Asmita Bhaskar, M. J. Chouhan, and N. J. Uke. "Image Retrieval Based on WBCH and Clustering Algorithm." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 5, no. 3 (September 15, 2013): 604–13. http://dx.doi.org/10.24297/ijmit.v5i3.761.
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Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.
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Zakariya, S. M., and Imtiaz A. Khan. "Analysis of combined approaches of CBIR systems by clustering at varying precision levels." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5009. http://dx.doi.org/10.11591/ijece.v11i6.pp5009-5018.
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<span lang="EN-US">The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and color-texture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named cluster-based retrieval of images by unsupervised learning (CLUE).</span>
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Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "A Retrieval Strategy for Texture Image." Applied Mechanics and Materials 635-637 (September 2014): 1018–25. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1018.
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Texture Information is widely used as one of the main low-layer features in the content-based image retrieval. In general, when the retrieval is carried out in texture image space, the same description method is adopted to regular and irregular texture images. As a large amount of regular and irregular texture images existed in the image database, it is very difficult to describe every texture with the same description method. In this paper, a retrieval strategy for texture image is proposed. The proposed strategy is divided into steps: First, classify texture images by Wold decomposition into regular and irregular texture images, then describe and retrieve them by regular and irregular texture description separately. Experimental results have showed that proposed strategy can improve classification and retrieval precision.
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Reddy, Tatireddy Subba, Sanjeevaiah K., Sajja Karthik, Mahesh Kumar, and Vivek D. "Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm." International Journal of Software Innovation 11, no. 1 (January 1, 2023): 1–15. http://dx.doi.org/10.4018/ijsi.315661.
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In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.
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Selvam, S., and S. Thabasukannan. "An Efficient Method for Color-Based Image Retrieval System." Asian Journal of Electrical Sciences 3, no. 2 (November 5, 2014): 38–45. http://dx.doi.org/10.51983/ajes-2014.3.2.1922.
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Content-based image retrieval systems retrieve images from a database that are determined to be similar to a query image based only on features extracted from the images. This paper focuses on color-based image retrieval. We define methods to improve the efficiency and effectiveness of color-based retrieval. We have tested our system using a collection of color images and query images. Color histograms are used to extract and store the color content of the images. Our empirical results are very encouraging. The main aim of this paper is to reduce substantially the total color space without degrading retrieval performance. In addition, we are able to improve performance by conducting object retrieval based solely on color.
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Jing, Chenchen, Yukun Li, Hao Chen, and Chunhua Shen. "Retrieval-Augmented Primitive Representations for Compositional Zero-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (March 24, 2024): 2652–60. http://dx.doi.org/10.1609/aaai.v38i3.28043.
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Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by learning from seen compositions. Composing the learned knowledge of seen primitives, i.e., attributes or objects, into novel compositions is critical for CZSL. In this work, we propose to explicitly retrieve knowledge of seen primitives for compositional zero-shot learning. We present a retrieval-augmented method, which augments standard multi-path classification methods with two retrieval modules. Specifically, we construct two databases storing the attribute and object representations of training images, respectively. For an input training/testing image, we use two retrieval modules to retrieve representations of training images with the same attribute and object, respectively. The primitive representations of the input image are augmented by using the retrieved representations, for composition recognition. By referencing semantically similar images, the proposed method is capable of recalling knowledge of seen primitives for compositional generalization. Experiments on three widely-used datasets show the effectiveness of the proposed method.
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Al-Obaide, Zahraa H., and Ayad A. Al-Ani. "COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS." Iraqi Journal of Information and Communication Technology 5, no. 1 (April 29, 2022): 16–30. http://dx.doi.org/10.31987/ijict.5.1.182.
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Searching for a relevant image in an archive is a problematic research issue for the computer vision research community. The majority of search engines retrieve images using traditional text-based approaches that rely on captions and metadata. Extensive research has been reported in the last two decades for content-based image retrieval (CBIR), analysis, and image classification. Content-Based Image Retrieval is a process that provides a framework for image search, and low-level visual features are commonly used to retrieve the images from the image database. The essential requirement in any image retrieval process is to sort the images with a close similarity in terms of visual appearance. The shape, color, and texture are examples of low-level image features. In image classification-based models and CBIR, high-level image visuals are expressed in the form of feature vectors made up of numerical values. The researcher's findings a significant gap between human visual comprehension and image feature representation. In this paper, we plan to present a comparison study and a comprehensive overview of the recent developments in the field of CBIR and image representation.
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Luo, Youmeng, Wei Li, Xiaoyu Ma, and Kaiqiang Zhang. "Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism." Information 13, no. 10 (September 24, 2022): 446. http://dx.doi.org/10.3390/info13100446.
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With the continuous progress of image retrieval technology, in the field of image retrieval, the speed of a search for a desired image from a great deal of image data becomes a hot issue. Convolutional Neural Networks(CNN) have been used in the field of image retrieval. However, many image retrieval systems based on CNN have a poor ability to express image features, resulting in a series of problems such as low retrieval accuracy and robustness. When the target image is retrieved from a large amount of image data, the vector dimension after image coding is high and the retrieval efficiency is low. Locality-sensitive hash is a method to find similar data from massive high latitude data. It reduces the data dimension of the original spatial data through hash coding and conversion, and can also maintain the similarity between the data. The retrieval time and space complexity are low. Therefore, this paper proposes a locality-sensitive hash image retrieval method based on CNN and the attention mechanism. The steps of the method are as follows: using the ResNet50 network as the feature extractor of the image, adding the attention module after the convolution layer of the model, and using the output of the network full connection layer to retrieve the features of the image database, then using the local-sensitive hash algorithm to hash code the image features of the database to reduce the dimension and establish the index, and finally measuring the features of the image to be retrieved and the image database to get the most similar image, completing the content-based image retrieval task. The method in this paper is compared with other image retrieval methods on corel1k and corel5k datasets. The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved. It also has higher robustness in different scenarios.
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Nurhaida, Ida, Hong Wei, Remmy A. M. Zen, Ruli Manurung, and Aniati M. Arymurthy. "Texture Fusion for Batik Motif Retrieval System." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3174. http://dx.doi.org/10.11591/ijece.v6i6.12049.
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<p>This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.</p>
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Nurhaida, Ida, Hong Wei, Remmy A. M. Zen, Ruli Manurung, and Aniati M. Arymurthy. "Texture Fusion for Batik Motif Retrieval System." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3174. http://dx.doi.org/10.11591/ijece.v6i6.pp3174-3187.
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<p>This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.</p>
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Minarno, Agus Eko, Muhammad Yusril Hasanuddin, and Yufis Azhar. "Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor." JOIV : International Journal on Informatics Visualization 7, no. 1 (February 6, 2023): 115. http://dx.doi.org/10.30630/joiv.7.1.1299.
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Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
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V, Vinitha, and Velantina V. "Content Based Image Retrieval from Auto Encoders Using Keras and Tensor Flow Python API a Deep Learning Technique." Volume 5 - 2020, Issue 9 - September 5, no. 9 (October 3, 2020): 869–71. http://dx.doi.org/10.38124/ijisrt20sep116.
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As the technology is evolving new methods and techniques are determined and implemented in a smart way to improve and achieve a greater insight in this smart era. The retrieval of image is popularly growing in this emerging trend. In this paper we have used how to build a very simple image retrieval system using a special type of Neural Network called auto encoders. Here the images can be retrieved with visual contents textures, shape and this method of image retrieval is called content based image retrieval.
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Kalimuthu, Manikandan, and Ilango Krishnamurthi. "Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance." Journal of Applied Mathematics 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/284378.
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The semantic-based facial image-retrieval system is concerned with the process of retrieving facial images based on the semantic information of query images and database images. The image-retrieval systems discussed in the literature have some drawbacks that degrade the performance of facial image retrieval. To reduce the drawbacks in the existing techniques, we propose an efficient semantic-based facial image-retrieval (SFIR) system using APSO and squared Euclidian distance (SED). The proposed technique consists of three stages: feature extraction, optimization, and image retrieval. Initially, the features are extracted from the database images. Low-level features (shape, color, and texture) and high-level features (face, mouth, nose, left eye, and right eye) are the two features used in the feature-extraction process. In the second stage, a semantic gap between these features is reduced by a well-known adaptive particle swarm optimization (APSO) technique. Afterward, a squared Euclidian distance (SED) measure will be utilized to retrieve the face images that have less distance with the query image. The proposed semantic-based facial image-retrieval (SFIR) system with APSO-SED will be implemented in working platform of MATLAB, and the performance will be analyzed.
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Chakraverti, Ashish. "Deep Learning based Smart Image Search Engine." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 1577–85. http://dx.doi.org/10.22214/ijraset.2024.58602.
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Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system
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Pan, Wenyan, Meimin Wang, Jiaohua Qin, and Zhili Zhou. "Improved CNN-Based Hashing for Encrypted Image Retrieval." Security and Communication Networks 2021 (February 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/5556634.
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As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.
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Babu, Chippy. "Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 14, 2021): 312–3189. http://dx.doi.org/10.22214/ijraset.2021.37321.
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Remote sensing image retrieval (RSIR) may be a fundamental task in remote sensing. Most content-based image retrieval (CBRSIR) approaches take an easy distance as similarity criteria. A retrieval method supported weighted distance and basic features of Convolutional Neural Network (CNN) is proposed during this letter. the strategy contains two stages. First, in offline stage, the pretrained CNN will be fine-tuned by some labelled images from our target data set, then accustomed extract CNN features, and labelled the pictures within the retrieval data set. Second, in online stage, we extract features of the query image by using fine-tuned CNN model and calculate the load of every image class and apply them to calculate the space between the query image and also the retrieved images. Experiments and methods are conducted on two Remote Sensing Image Retrieval data sets. Compared with the state-of the-art methods, the proposed method significantly improves retrieval performance.
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Maji, Subhadip, and Smarajit Bose. "CBIR Using Features Derived by Deep Learning." ACM/IMS Transactions on Data Science 2, no. 3 (August 31, 2021): 1–24. http://dx.doi.org/10.1145/3470568.
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In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.” In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.
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Nhi, Nguyen Thi Uyen, Thanh Manh Le, and Thanh The Van. "A Model of Semantic-Based Image Retrieval Using C-Tree and Neighbor Graph." International Journal on Semantic Web and Information Systems 18, no. 1 (January 2022): 1–23. http://dx.doi.org/10.4018/ijswis.295551.
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The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.
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Zhu, Chang, Wenchao Jiang, Weilin Zhou, and Hong Xiao. "Similarity Retrieval Based on Image Background Analysis." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–14. http://dx.doi.org/10.4018/ijssci.309426.
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Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.
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Makandar, Dr Aziz, Mrs Rashmi Somshekhar, and Miss Nayan Jadav. "Content Based Image Retrieval." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 1151–54. http://dx.doi.org/10.31142/ijtsrd24047.
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Hung, Chia-Ching. "A study on a content-based image retrieval technique for Chinese paintings." Electronic Library 36, no. 1 (February 5, 2018): 172–88. http://dx.doi.org/10.1108/el-10-2016-0219.
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Purpose The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search images similar to the query image based on colour histogram and texture features in the database. Thus, retrieving images by this image technique is expected to make the retrieval of Chinese painting images more precise and convenient for users. Design/methodology/approach In this study, a technique is proposed that considers spatial information of colours in addition to texture feature in image retrieval. This technique can be applied to retrieval of Chinese painting images. A database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. The authors develop an image-retrieval technique that considers colour distribution, spatial information of colours and texture. Findings In this study, a database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. An image-retrieval technique was developed that considers colour distribution, spatial information of colours and texture. Through adjustment of feature values, this technique is able to process both landscape and portrait images. This technique also addresses liubai (i.e. blank) and text problems in the images. The experimental results confirm high precision rate of the proposed retrieval technique. Originality/value In this paper, a novel Chinese painting image-retrieval technique is proposed. Existing image-retrieval techniques and the features of Chinese painting are used to retrieve Chinese painting images. The proposed technique can exclude less important image information in Chinese painting images for instance liubai and calligraphy while calculating the feature values in them. The experimental results confirm that the proposed technique delivers a retrieval precision rate as high as 92 per cent and does not require a considerable computing power for feature extraction. This technique can be applied to Web page image retrieval or to other mobile applications.
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Rao, Thiriveedhi Yellamanda Srinivasa, and Pakanati Chenna Reddy. "Classification and Retrieval of Images Based on Extensive Context and Content Feature Set." Recent Patents on Computer Science 12, no. 3 (May 8, 2019): 162–70. http://dx.doi.org/10.2174/2213275911666181107114537.
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Background: This paper renders a classification and retrieval of image achievements in the search area of image retrieval, especially content-based image retrieval, an area that has been very active and successful in the past few years. Objective: Primarily the features extracted established on the bag of visual words (BOW) can be arranged by utilizing Scaling Invariant Feature Transform (SIFT) and developed K-Means clustering method. Methods: The texture is extracted for a developed multi-texton method by our study. Our retrieval process consists of two stages such as retrieval and classification. The images will be classified established on the features by applying k- Nearest Neighbor (kNN) algorithm. This will separate the images into various classes in order to develop the precision and recall rate initially. Results: After the classification of images, the similar images are retrieved from the relevant class as per the afforded query image.
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Alikhani, Malihe, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, and Matthew Stone. "Cross-Modal Coherence for Text-to-Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10427–35. http://dx.doi.org/10.1609/aaai.v36i10.21285.
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Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model. However, co-occurring images and text can be related in qualitatively different ways, and explicitly modeling it could improve the performance of current joint understanding models. In this paper, we train a Cross-Modal Coherence Model for text-to-image retrieval task. Our analysis shows that models trained with image–text coherence relations can retrieve images originally paired with target text more often than coherence-agnostic models. We also show via human evaluation that images retrieved by the proposed coherence-aware model are preferred over a coherence-agnostic baseline by a huge margin. Our findings provide insights into the ways that different modalities communicate and the role of coherence relations in capturing commonsense inferences in text and imagery.