Journal articles on the topic 'IMAGE RETRIEVAL TECHNIQUES'
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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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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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Meharban, M. S., and Dr S. Priya. "A Review on Image Retrieval Techniques." Bonfring International Journal of Advances in Image Processing 6, no. 2 (April 30, 2016): 07–10. http://dx.doi.org/10.9756/bijaip.8136.
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Dureja, Aman, and Payal Pahwa. "Image retrieval techniques: a survey." International Journal of Engineering & Technology 7, no. 1.2 (December 28, 2017): 215. http://dx.doi.org/10.14419/ijet.v7i1.2.9231.
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In the recent years, the development in computer technologies and multimedia applications has led to the production of huge digital images and large image databases, and it is increasing rapidly. There are several different areas in which image retrieval plays a crucial role like Medical systems, Forensic Labs, Tourism Promotion, etc. Thus retrieval of similar images is a challenge. To tackle this rapid growth in digital repositories it is necessary to develop image retrieval systems, which can operate on large databases. There are basically three techniques, which is useful for efficient retrieval of images. With these techniques, the number of methods has been modified for the efficient image retrieval of images. In this paper, we presented the survey of different techniques that has been used starting from Image retrieval using visual features and latest by the deep learning with CNN that contains the number of layers and now becomes the best base method for retrieval of images from the large databases. In the last section, we have made the analysis between various developed techniques and showed the advantages and disadvantages of various techniques.
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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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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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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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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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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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Md Jan, Mardhiyah, Nasharuddin Zainal, and Shahrizan Jamaludin. "Region of interest-based image retrieval techniques: a review." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (September 1, 2020): 520. http://dx.doi.org/10.11591/ijai.v9.i3.pp520-528.
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<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>
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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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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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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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Kaur, Gagandeep, and Rajeev Kumar Dang. "Feature Based Comparison of Text Based Image Retrieval and Context Based Image Retrieval Images." Asian Journal of Engineering and Applied Technology 7, no. 2 (October 5, 2018): 6–11. http://dx.doi.org/10.51983/ajeat-2018.7.2.965.
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Image processing is a field to process the images according to horizontal and vertical axis to form some useful results. It deals with edge detection, image compression, noise removal, image segmentation, image identification, image retrieval and image variation etc. Customarily, there are two techniques i.e. text based image retrieval and content based image retrieval that are used for retrieving the image according to features and providing color to all pixel pairs. The system retrieval that is based on TBIR assists to recover an image from the database using annotations. CBIR extorts images to form a hefty degree database using the visual contents of an original image that is called low level features or features of an image. These visual features are extracted using feature extraction and then match with the input image. Histogram, color moment, color correlogram, Gabor filter and wavelet transform are various CBIR techniques that can be used autonomously or pooled to acquire enhanced consequences. This paper states about a novel technique for fetching the images from the image database using two low level features namely color based feature and texture based features. Two techniques- one is color correlogram (for color indexing) and another is wavelet transform (for texture processing) has also been introduced.
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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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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.
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Sandhu, Amanbir, and Aarti Kochhar. "Content Based Image Retrieval using Texture, Color and Shape for Image Analysis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 149–52. http://dx.doi.org/10.24297/ijct.v3i1c.2768.
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Content- Based Image Retrieval(CBIR) or QBIR is the important field of research..Content Based Image retrieval has gained much popularity in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties over color, texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output with a single feature
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Layona, Rita, Yovita Tunardi, and Dian Felita Tanoto. "Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur." ComTech: Computer, Mathematics and Engineering Applications 5, no. 2 (December 1, 2014): 1073. http://dx.doi.org/10.21512/comtech.v5i2.2369.
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Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR), but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR). In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved.
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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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Salih, Shalaw Faraj, and Alan Anwer Abdulla. "An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept." UHD Journal of Science and Technology 5, no. 1 (January 5, 2021): 1–12. http://dx.doi.org/10.21928/uhdjst.v5n1y2021.pp1-12.
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Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.
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James, I. Samuel Peter. "Face Image Retrieval with HSV Color Space using Clustering Techniques." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 01 (April 16, 2013): 21–24. http://dx.doi.org/10.9756/sijcsea/v1i1/01010253.
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Malik, C. K. Mohammed. "Content based Image Retrieval Using Clustering Method." International Academic Journal of Science and Engineering 6, no. 2 (September 26, 2022): 06–12. http://dx.doi.org/10.9756/iajse/v6i2/1910020.
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Content-based image retrieval (CBIR) is the deployment of computer vision methods to the information retrieval challenge, that is, the subject of seeking out digital images in vast databases. Techniques based on automated feature extraction methods for obtaining similar images from image databases are under the purview of CBIR. Traditional content based image retrieval (CBIR) systems extract a single feature at a time and use it to categorize and group images in response to a query. To bridge the gap between high-level concepts and low-level features, our innovative method integrates many feature extraction algorithms. In color-based retrieval, we use quadratic distance formulas to calculate the HSV affinity matrix for photos in the query and the database. Wavelet decomposition at six stages is used in texture-based retrieval. Finding the similarity measures between the query image and the images in the database is done with the help of the Euclidean distance classifier. The integrated method used to decrease the file sizes of the retrieved photographs keeps the user from having to pay as much attention to the process.
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Du, Liying, and Nabila H. Saleh. "Medical Image Retrieval Algorithm Based on Content." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 675–79. http://dx.doi.org/10.2174/1874129001408010675.
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With the rapid development of science and technology and the improving medical service, medical image's role in clinical diagnosis and treatment becomes increasingly prominent. It has become a high-profile task to help the doctors pick out desired targets from massive medical images. Currently, techniques of text-based medical image retrieval have failed to meet the need of massive medical image retrieval. On the other hand, techniques of content-based medical image retrieval are already established and hold vast research potential. Starting with the introduction of matured techniques of medical image retrieval, the paper expounds on the evaluation criteria of the effectiveness of such techniques, then on the modified text-and-content-based medical image retrieval algorithm. The last part is the verification of the research conclusion with contrast experiments illustrated by the sample figures.
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Safaei, Ali Asghar, and Saeede Habibi-Asl. "Multidimensional indexing technique for medical images retrieval." Intelligent Data Analysis 25, no. 6 (October 29, 2021): 1629–66. http://dx.doi.org/10.3233/ida-205495.
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Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.
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., Yuvraj, Ishu Bansal, Sakshi Dhar, and Gitanjali Nikam. "AN IMPLEMENTATION PAPER ON A FRAMEWORK AND TECHNIQUES FOR IMAGE-BASED SEARCH APPLICATION WITH AN E-COMMERCE DOMAIN." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (October 1, 2021): 238–53. http://dx.doi.org/10.33564/ijeast.2021.v06i06.034.
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A large image database is growing rapidly as billions of images transferred every day at various chronicles like Instagram, snapchat, Facebook etc. and expected to continue in the future. With a particularly immense measure of data, the requirement for a successful pursuit in the image ages. Furthermore, if astoundingapparatuses have effectively been made for text search, image search stays an uncertain issue. Traditionally, most end-users use retrieval systems to write questions or queries and get text results. But endusers continuously expect search engines to be "intelligent" and they want a retrieval system to explore cyberspace using an image, as with the built-in camera on a mobile phone to send relevant resemblance, videos, and other formats. So, it becomes a major challenge to retrieve and processing images from this large database. Our paper introduced a novel methodology in contentbased image retrieval (CBIR) by consolidating the low level component for example shape, texture and color features. CBIR helps in retrieving images from an huge database in an efficient manner it is fastest growing research area in this field. The study found that convolutional neural networks can use to break down divisions and retrieval issues. Image details are much bigger than text data, and we cannot identify ocular particular by old techniques designed to identify text details. Therefore, CBIR has acquired significant benefits from research society. In this paper we aim to frame the feature vectors of the entire image by consolidating the shape, texture and color features and to get a decent performance in terms of the precision and recall. The image retrieval system is used to find images as per user request from the database. In this paper we also explored the flutter framework with dart language. We aim to mitigate the fundamental issue that app developers have been facing for so long- maintaining multiple apps for multiple platforms.
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Yu, Jiaohui. "Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping." Advances in Mathematical Physics 2021 (September 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/2834873.
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In today’s rapid development of network and multimedia technology, the booming of electronic commerce, users in the network shopping species of images and other multimedia information showing geometric growth, in the face of this situation, how to find the images they need in the vast amount of online shopping images has become an urgent problem to solve. This paper is based on the partial differential equation to do the following research: Based on the partial differential equation is a kind of equation that simulates the human visual perception system to analyze images; based on the summary of the advantages and disadvantages of multifeature image retrieval technology, we propose a multifeature image retrieval technology method based on the partial differential equation to alleviate the indexing imbalance caused by the mismatch of multifeature image retrieval technology distribution. To improve the search speed of the data-dependent locally sensitive hashing algorithm, we propose a query pruning algorithm compatible with the proposed partial differential equation-based multifeature image retrieval technology method, which greatly improves the retrieval speed while ensuring the retrieval accuracy; to implement the data-dependent partial differential equation algorithm, we need to distribute the data set among different operation nodes, and to better achieve better parallelization of operations, we need to measure the similarity between categories, and we achieve the problem of distributing data among various categories in each operation node by introducing a clustering method with constraints. The purpose of this article for image recognition is for better shopping platforms for merchants. This algorithm has trained multiple samples and has data support. The experimental results show that our proposed data set allocation method shows significant advantages over the data set allocation method that does not consider category correlation. However, the image features used in image retrieval systems are often hundreds or even thousands of dimensions, and these features are not only high in dimensionality but also huge in number, which makes image retrieval systems encounter an inevitable problem—“dimensionality disaster.” To overcome this problem, scholars have proposed a series of approximate nearest neighbor methods, but multifeature image retrieval techniques based on partial differential equations are more widely used in people’s daily life.
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Yasmin, Mussarat, Sajjad Mohsin, and Muhammad Sharif. "Intelligent Image Retrieval Techniques: A Survey." Journal of Applied Research and Technology 12, no. 1 (February 2014): 87–103. http://dx.doi.org/10.1016/s1665-6423(14)71609-8.
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Dewan, Jaya H., and Sudeep D. Thepade. "Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review." Applied Computational Intelligence and Soft Computing 2020 (October 22, 2020): 1–20. http://dx.doi.org/10.1155/2020/8851931.
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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.
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Zeng, Yan Sheng, and Xu Lin Ying. "Recent Improvements in Image Retrieval." Advanced Materials Research 989-994 (July 2014): 4069–73. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4069.
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The target of this paper is to introduce the improvement of the technique of image retrieval. At first, it comes up with the concept of image retrieval and shows the importance of this technique. As the techniques of multimedia and Internet are developing rapidly, the resources of images that users obtain are also extended. And then this paper gives the problem about the image retrieval, namely the information of images are disordering. As the result, it is significant to do the effective organization, management and retrieval based on the increasingly extensive image information storage. After that, this paper presents the concept of TBIR and CBIR and gives the definitions of them. It proposes an issue that CBIR is the improvement of TBIR. Based on CBIR, there are also some disadvantages that need to be improved. In terms of the main point of CBIR, the paper raises that the annotation is one of the most difficult techniques that need to be promoted. Then it describes some algorithms about the technique of automatic image annotation. After these algorithms, the paper shows the challenges and developing direction of the technique of image retrieval. At last, it presented the conclusion to emphasize the main points of this paper.
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Mai, Nicole Tham Ley, Syahmi Syahiran Bin Ahmad Ridzuan, and Zaid Bin Omar. "Content-based Image Retrieval System for an Image Gallery Search Application." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (June 1, 2018): 1903. http://dx.doi.org/10.11591/ijece.v8i3.pp1903-1912.
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Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient browsing through three different search modes: retrieval by image query, retrieval by facial recognition, and retrieval by text or tags. In this project, the MPEG-7-like Powered Localized Color and Edge Directivity Descriptor is used to extract the feature vectors of the image database and the facial recognition system is built around the Eigenfaces concept. A graphical user interface with the basic functionality of an image gallery application is also developed to implement the three search modes. Results show that the application is able to retrieve and display images in a collection as thumbnail previews with high retrieval accuracy and medium relevance and the computational requirements for subsequent searches were significantly reduced through the incorporation of text-based image retrieval as one of the search modes. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.
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Thangarajan, Ahilandeswari, and Vivekanandan Kalimuthu. "CBIR with Partial Input of Unshaped Images Using Compressed-Pixel Matching Algorithm." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 206. http://dx.doi.org/10.14419/ijet.v7i3.27.17762.
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Many works have been done to find out whether given image is in the database using Content Based Image Retrieval (CBIR) techniques. However if the query image is unshaped or noise filled then retrieval of that image in the database is difficult .We propose an approach by which for any shape of input image the databases is searched and the most relevant image is retrieved. Results provides better accuracy than existing one and time elapsed also reduced because of making comparison after compression of both partial image and images from the database. The attainment of the proposed system is assessed using LFW and WANG image sets consisting of 2000 and 9990 images, respectively, and it measured with familiar methods with regard to precision and recall which demonstrates the advantages of the proposed approach.
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Wang, Jian Feng, Wen Ming Wu, and Xiao Rong Zhao. "Applied Technology in Image Retrieval via 8-Direction Gaussian Density in the DCT Domain." Advanced Materials Research 977 (June 2014): 431–34. http://dx.doi.org/10.4028/www.scientific.net/amr.977.431.
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In this paper, A new algorithm for compressed image retrieval is proposed based on Gaussian Density Feature Vector(GDFV). This algorithm directly extract gaussian density of 8 direction from compressed image data to construct a 2-dimention array (8*4) as an indexing key to retrieve images based on their content features. To test and evaluate the proposed algorithms, we carried out experiments with a database of 1000 images. In comparison with existing representative techniques, the experimental results show the superiority of the proposed method in terms of retrieval precision and processing speed.
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Chopra, Sumit. "CONTENT-BASED IMAGE RETRIEVAL TECHNIQUES FOR MAMMOGRAPHIC IMAGES USING SOFT COMPUTING TECHNIQUES." International Journal of Advanced Research in Computer Science 8, no. 9 (September 30, 2017): 674–78. http://dx.doi.org/10.26483/ijarcs.v8i9.5132.
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Chouhan, Preeti, and Mukesh Tiwari. "Image Retrieval Using Data Mining and Image Processing Techniques." IJIREEICE 3, no. 12 (December 15, 2015): 53–58. http://dx.doi.org/10.17148/ijireeice.2015.31212.
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Patil, Sheetal Deepak. "Content Based Image and Video Retrieval A Compressive Review." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 243–47. http://dx.doi.org/10.35940/ijeat.e2783.0610521.
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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy data and inaccuracy, which often result in the showing of irrelevant images or videos. The authors of the proposed study integrate image and visual data to improve the precision of the retrieved results for both photographs and videos. In response to a user's query, this study investigates alternative ways for fetching high-quality photos and related videos.
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Wang, Xue Li, and Da Qian Wang. "Intuitive Visualization for Online Image Retrieval." Applied Mechanics and Materials 40-41 (November 2010): 549–53. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.549.
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We proposed a efficient and intuitive method to show the Online image retrieval result for searching information. The method incorporates visualization techniques in content-based image retrieval to show the hidden information in the result. We incorporated image browsing into online image retrieval. We give users a initial display based on PageRank, then use the users’ feedback to compute similarity function, then we compute the dissimilarity between images, get the position of images in the display space. If users are not satisfied with the display, they may feed back some more interested images to the system to improve the display. With the interactions provided by the system, users can browse a large number of images efficiently and find the exact images fast.
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Kumar, Suneel, Manoj Kumar Singh, and Manoj Kumar Mishra. "Improve Content-based Image Retrieval using Deep learning model." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2327/1/012028.
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Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
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Chen, Yen-Wei, Xinyin Huang, Dingye Chen, and Xian-Hua Han. "Generic and Specific Impressions Estimation and Their Application to KANSEI-Based Clothing Fabric Image Retrieval." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 10 (June 20, 2018): 1854024. http://dx.doi.org/10.1142/s0218001418540241.
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Current image retrieval techniques are mainly based on text or visual contents. However, both text-based and contents-based methods lack the capability of utilizing human intuition and KANSEI (impression). In this paper, we proposed an impression-based image retrieval method in order to realize the image retrieval according to our impression presented by impression keywords. We first propose a generic and specific impressions estimation method based on machine learning and then apply it to impression-based clothing fabric image retrieval. We use a semantic differential (SD) method to measure the user’s impressions such as brightness and warmth while they view a cloth fabric image. We also extract both global and local features of cloth fabric images such as color and texture using computer vision techniques. Then we use support vector regression to model the mapping functions between the generic impression (or specific impression) and image features. The learnt mapping functions are used to estimate the generic and specific impressions of cloth fabric images. The retrieval is done by comparing the query impression with the estimated impression of images in the database.
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Seth, Nitika, and Sonika Jindal. "A REVIEW ON CONTENT BASED IMAGE RETRIEVAL." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 14 (January 17, 2017): 7498–503. http://dx.doi.org/10.24297/ijct.v15i14.5640.
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Image retrieval means to recover the original image from the reconstructed image, here in this paper we have discussed latest techniques in the field of image retrieval for image processing. Content Based Image Retrieval (CBIR) is one of the most exciting and fastest growing research areas in the field of Image Processing. The techniques presented are Boosting image retrieval, soft query in image retrieval system, content based image retrieval by integration of metadata encoded multimedia features, and object based image retrieval and Bayesian image retrieval system. Some probable future research directions are also presented here to explore research area in the field of image retrieval
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Kalra, Meenu, and Pooja Handa. "A Survey on Features and Techniques in Content Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 10 (June 27, 2015): 6129–34. http://dx.doi.org/10.24297/ijct.v14i10.1829.
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Content-based image retrieval (CBIR) is widely adopted method for finding images from vast collection of images in the database. As the collections of images are growing at a rapid rate, demand for efficient and effective tools for retrieval of query images from database is increased significantly. Among them, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images as it requires relatively less human intervention. The requirement for development of CBIR is enhanced due to tremendous growth in volume of images as well as the widespread application in multiple fields. Texture, color, shaped, contours etc are the important entities to represent and search the images. These features of images are extracted and implemented for a similarity check among images. In this paper, we have conducted a survey on the CBIR techniques and its approaches and their usage in various domains.
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Singh, Vibhav Prakash, Rajeev Srivastava, Yadunath Pathak, Shailendra Tiwari, and Kuldeep Kaur. "Content-based image retrieval based on supervised learning and statistical-based moments." Modern Physics Letters B 33, no. 19 (July 8, 2019): 1950213. http://dx.doi.org/10.1142/s0217984919502130.
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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.
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Battur, Ranjana, and Jagadisha Narayana. "Classification of medical X-ray images using supervised and unsupervised learning approaches." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (June 1, 2023): 1713. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1713-1721.
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Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.
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Dharani, T., R. Kiruba Kumari, B. Sindhupiriya, and R. Mahalakshmi. "Pattern Based Image Retrieval System by Using Clustering Techniques." Data Analytics and Artificial Intelligence 3, no. 2 (January 1, 2023): 44–49. http://dx.doi.org/10.46632/daai/3/2/9.
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Nowadays, the image database is growing in enormous size with heterogeneous categories. Similarly, there is rising demands from users in various ways. The most demanding domains in society are health care, Agriculture, commerce, and security. Healthcare domain is concerned with diagnosing the disease. Security domain is involved in investigation of the Criminals. Commerce domain needs analysis to recognize the right product. Agriculture domain requires processing of disease affected fruit images. The PBIR system that combines evidence from multiple digital image domains can reduce those problems of existing image retrieval systems. The PBIR system is used to find uncertain parts of the image during augmentation steps. The result is identified with various parameters (e.g., accuracy, precision, distance, sum of distance), showing that the performance of the k-medoids pam algorithm better than the existing iterative clustering algorithms
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Edmundson, David, and Gerald Schaefer. "JIRL." International Journal of Multimedia Data Engineering and Management 4, no. 2 (April 2013): 1–12. http://dx.doi.org/10.4018/jmdem.2013040101.
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Since there are few open image retrieval toolkits available, researchers in the field are often forced to re-implement existing algorithms in order to perform a comparative evaluation. None of the existing toolkits support retrieval of JPEG images directly in the compressed domain. The authors’ aim is therefore to facilitate the use of compressed domain image retrieval techniques as well as ease retrieval evaluation by fellow researchers. For this purpose, the authors present JIRL, an open source C++ software suite that allows content-based image retrieval in the JPEG compressed domain and provides tools for benchmarking retrieval accuracy and retrieval time. In total, twelve state-of-the-art JPEG retrieval algorithms are implemented, while for each method techniques for compressed domain feature extraction as well as feature comparison are provided in an object-oriented framework. An example image retrieval application is also provided to demonstrate how the library can be used. JIRL is made available to fellow researchers under the LGPL v.2.1 license.
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Serata, Manabu, Yutaka Hatakeyama, and Kaoru Hirota. "Designing Image Retrieval System with the Concept of Visual Keys." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 2 (March 20, 2006): 136–44. http://dx.doi.org/10.20965/jaciii.2006.p0136.
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A concept of visual keys is proposed to provide efficient and useful content-based image retrieval systems to users. Visual keys are defined as representative sub-images which are extracted from an image database by using image feature clustering. The proposed system is implemented and is tested on 1,000 images, which are included in the COREL database. Although the system makes use of only 80 sub-images from 8,962 ones extracted from the image database, the performance is kept with 90%. The retrieval time is within 4ms on the proposed system, which has retrieval efficiency like that of text retrieval by being applied text retrieval techniques, and thus the system is expected to provide the services on the WWW.
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Sivakumar, M., N. M. Saravana Kumar, and N. Karthikeyan. "Content-Based Image Retrieval Techniques: A Survey." Journal of Physics: Conference Series 1964, no. 4 (July 1, 2021): 042027. http://dx.doi.org/10.1088/1742-6596/1964/4/042027.
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., Aiswarya V. "SURVEY ON CONTENT BASED IMAGE RETRIEVAL TECHNIQUES." International Journal of Research in Engineering and Technology 03, no. 19 (May 25, 2014): 754–57. http://dx.doi.org/10.15623/ijret.2014.0319135.
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Khaleel, Baydaa. "COLOR IMAGE RETRIEVAL BASED ON FUZZY NEURAL NETWORK AND SWARM INTELLIGENCE TECHNIQUES." IIUM Engineering Journal 23, no. 1 (January 4, 2022): 116–28. http://dx.doi.org/10.31436/iiumej.v23i1.1802.
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Image retrieval is an important system for retrieving similar images by searching and browsing in a large database. The image retrieval system can be a reliable tool for people to optimize the use of image accumulation, and finding efficient methods to retrieve images is very important. Recent decades have marked increased research interest in field image retrieval. To retrieve the images, an important set of features is used. In this work, a combination of methods was used to examine all the images and detect images in a database according to a query image. Linear Discriminant Analysis (LDA) was used for feature extraction of the images into the dataset. The images in the database were processed by extracting their important and robust features and storing them in the feature store. Likewise, the strong features were extracted for specific query images. By using some Meta Heuristic algorithms such as Cuckoo Search (CS), Ant Colony Optimization (ACO), and using an artificial neural network such as single-layer Perceptron Neural Network (PNN), similarity was evaluated. It also proposed a new two method by hybridized PNN and CS with fuzzy logic to produce a new method called Fuzzy Single Layer Perceptron Neural Network (FPNN), and Fuzzy Cuckoo Search to examine the similarity between features for query images and features for images in the database. The efficiency of the system methods was evaluated by calculating the precision recall value of the results. The proposed method of FCS outperformed other methods such as (PNN), (ACO), (CS), and (FPNN) in terms of precision and image recall. ABSTRAK: Imej dapatan semula adalah sistem penting bagi mendapatkan imej serupa melalui carian imej dan melayari pangkalan besar data. Sistem dapatan semula imej ini boleh dijadikan alat boleh percaya untuk orang mengoptimum penggunaan pengumpulan imej, dan kaedah pencarian yang berkesan bagi mendapatkan imej adalah sangat penting. Beberapa dekad yang lalu telah menunjukan banyak penyelidikan dalam bidang imej dapatan semula. Bagi mendapatkan imej-imej ini, ciri-ciri set penting telah digunakan. Kajian ini menggunakan beberapa kaedah bagi memeriksa semua imej dan mengesan imej dalam pangkalan data berdasarkan imej carian. Kami menggunakan Analisis Diskriminan Linear (LDA) bagi mengekstrak ciri imej ke dalam set data. Imej-imej dalam pangkalan data diproses dengan mengekstrak ciri-ciri penting dan berkesan daripadanya dan menyimpannya dalam simpanan ciri. Begitu juga, ciri-ciri penting ini diekstrak bagi imej carian tertentu. Persamaan dinilai melalui beberapa algoritma Meta Heuristik seperti Carian Cuckoo (CS), Pengoptimuman Koloni Semut (ACO), dan menggunakan lapisan tunggal rangkaian neural buatan seperti Rangkaian Neural Perseptron (PNN). Dua cadangan baru dengan kombinasi hibrid PNN dan CS bersama logik kabur bagi menghasilkan kaedah baru yang disebut Lapisan Tunggal Kabur Rangkaian Neural Perceptron (FPNN), dan Carian Cuckoo Kabur bagi mengkaji persamaan antara ciri carian imej dan imej pangkalan data. Nilai kecekapan kaedah sistem dinilai dengan mengira ketepatan mengingat pada dapatan hasil. Kaedah FCS yang dicadangkan ini mengatasi kaedah lain seperti (PNN), (ACO), (CS) dan (FPNN) dari segi ketepatan dan ingatan imej.
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Maiwald, Ferdinand, Christoph Lehmann, and Taras Lazariv. "Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods." ISPRS International Journal of Geo-Information 10, no. 11 (November 4, 2021): 748. http://dx.doi.org/10.3390/ijgi10110748.
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The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.
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N, Parvin, and Kavitha P. "Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (July 1, 2017): 226. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp226-233.
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<p>Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.</p><p> </p>