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Добірка наукової літератури з теми "IMAGE RETRIEVAL TECHNIQUES"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
<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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Анотація:
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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Анотація:
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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Анотація:
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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9

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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Анотація:
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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Анотація:
<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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Дисертації з теми "IMAGE RETRIEVAL TECHNIQUES"

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Shaffrey, Cian William. "Multiscale techniques for image segmentation, classification and retrieval." Thesis, University of Cambridge, 2003. https://www.repository.cam.ac.uk/handle/1810/272033.

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Yang, Cheng 1974. "Image database retrieval with multiple-instance learning techniques." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50505.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 81-82).
In this thesis, we develop and test an approach to retrieving images from an image database based on content similarity. First, each picture is divided into many overlapping regions. For each region, the sub-picture is filtered and converted into a feature vector. In this way, each picture is represented by a number of different feature vectors. The user selects positive and negative image examples to train the system. During the training, a multiple-instance learning method known as the Diverse Density algorithm is employed to determine which feature vector in each image best represents the user's concept, and which dimensions of the feature vectors are important. The system tries to retrieve images with similar feature vectors from the remainder of the database. A variation of the weighted correlation statistic is used to determine image similarity. The approach is tested on a large database of natural scenes as well as single- and multiple-object images. Comparisons are made against a previous approach, and the effects of tuning various training parameters, as well as that of adjusting algorithmic details, are also studied.
by Cheng Yang.
S.M.
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3

Carswell, James. "Using Raster Sketches for Digital Image Retrieval." Fogler Library, University of Maine, 2000. http://www.library.umaine.edu/theses/pdf/CarswellJD2000.pdf.

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Zhang, Dengsheng 1963. "Image retrieval based on shape." Monash University, School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8688.

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Lim, Suryani. "Feature extraction, browsing and retrieval of images." Monash University, School of Computing and Information Technology, 2005. http://arrow.monash.edu.au/hdl/1959.1/9677.

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Goncalves, Pinheiro Antonio Manuel. "Shape approximation and retrieval using scale-space techniques." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391661.

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Li, Yuanxi. "Semantic image similarity based on deep knowledge for effective image retrieval." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/99.

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Анотація:
A flourishing World Wide Web dramatically increases the amount of images up­loaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the seman­tic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrieval, it is necessary to dig the deep concept and semantic meaning of the image as well as to overcome the semantic gap. By exploiting the context of Web images, knowledge base and ontology-based similarities, through the analysis of user behavior of image similarity evaluation, we established a set of formulas which allows e.cient and accurate semantic similarity measurement of images. When jointly applied with ontology-based query expansion approaches and an adaptive image search engine for deep knowledge indexing, they are able to produce a new level of meaningful automatic image annotation, from which semantic image search may be performed. Besides, the semantic concept can be automatically enriched in MPEG-7 Structured Image Annotation approach. The system is evaluated quantitatively using more than thousands of Web images with associated human tags with user subjective test. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good precision e.ciency. This approach enables an advanced degree of semantic richness to be automatically associated with images and e.cient image concept similarity measurement which could previously only be performed manually. Keywords: Image Index, Image Retrieval, Semantic Similarity, Relevance Feed­back, Knowledge Base, Ontology, Query Expansion, MPEG-7 . . .
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Wong, Chun Fan. "Automatic semantic image annotation and retrieval." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1188.

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Ling, Haibin. "Techniques for image retrieval deformation insensitivity and automatic thumbnail cropping /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3859.

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Анотація:
Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Liu, Danzhou. "EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2991.

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Анотація:
In content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database contains a large number of entries, and the search needs many iterations, and when the underlying distance measure is computationally expensive. The overall processing costs, including CPU and disk I/O, are further emphasized if there are numerous concurrent accesses. To address these limitations involved in relevance feedback processing, we propose a generic framework, including a query model, index structures, and query optimization techniques. Specifically, this thesis has five main contributions as follows. The first contribution is an efficient target search technique. We propose four target search methods: naive random scan (NRS), local neighboring movement (LNM), neighboring divide-and-conquer (NDC), and global divide-and-conquer (GDC) methods. All these methods are built around a common strategy: they do not retrieve checked images (i.e., shrink the search space). Furthermore, NDC and GDC exploit Voronoi diagrams to aggressively prune the search space and move towards target images. We theoretically and experimentally prove that the convergence speeds of GDC and NDC are much faster than those of NRS and recent methods. The second contribution is a method to reduce the number of expensive distance computation when answering k-NN queries with non-metric distance measures. We propose an efficient distance mapping function that transfers non-metric measures into metric, and still preserves the original distance orderings. Then existing metric index structures (e.g., M-tree) can be used to reduce the computational cost by exploiting the triangular inequality property. The third contribution is an incremental query processing technique for Support Vector Machines (SVMs). SVMs have been widely used in multimedia retrieval to learn a concept in order to find the best matches. SVMs, however, suffer from the scalability problem associated with larger database sizes. To address this limitation, we propose an efficient query evaluation technique by employing incremental update. The proposed technique also takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be accessed for query processing. This index structure also provides an inexpensive means to process the set of candidates to evaluate the final query result. This technique can work with different kernel functions and kernel parameters. The fourth contribution is a method to avoid local optimum traps. Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps that may severely impair the overall retrieval performance. We therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to continue the search for additional matching images, thus escaping from the local optimum. We also propose an index structure to speed up such neighborhood search. Finally, the fifth contribution is a generic framework to support concurrent accesses. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time while achieving better precision and recall, and is scalable to support a large user community. This latter performance characteristic is largely neglected in existing systems making them less suitable for large-scale deployment. With the growing interest in Internet-scale image search applications, our framework offers an effective solution to the scalability problem.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
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Книги з теми "IMAGE RETRIEVAL TECHNIQUES"

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Eakins, J. P. Techniques for image retrieval. London: Library Information Technology Centre, 1998.

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2

Newell, J. C. W. Archival retrieval: Techniques for image enhancement. London: British Broadcasting Corporation Research and Development Department, 1995.

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Enrico, Vicario, ed. Image description and retrieval. New York: Plenum Press, 1998.

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4

Integrated region-based image retrieval. Boston: Kluwer Academic Publishers, 2001.

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5

Intelligent image databases: Towards advanced image retrieval. Boston: Kluwer Academic Publishers, 1998.

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6

Gong, Yihong. Intelligent Image Databases: Towards Advanced Image Retrieval. Boston, MA: Springer US, 1998.

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7

1974-, Li Jia, and Wang James Z. 1972-, eds. Machine learning and statistical modeling approaches to image retrieval. Boston: Kluwer Academic Publishers, 2004.

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8

Vittorio, Castelli, and Bergman Lawrence D. 1953-, eds. Image databases: Search and retrieval of digital imagery. New York: Wiley, 2002.

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9

A, Hanjalic, ed. Image and video databases: Restoration, watermarking, and retrieval. Amsterdam: Elsevier Science B. V., 2000.

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Visual information retrieval. San Francisco, Calif: Morgan Kaufmann Publishers, 1999.

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Частини книг з теми "IMAGE RETRIEVAL TECHNIQUES"

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Safar, Maytham H., and Cyrus Shahabi. "Image Description Techniques." In Shape Analysis and Retrieval of Multimedia Objects, 3–7. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0349-1_1.

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Tyagi, Vipin. "Content-Based Image Retrieval Techniques: A Review." In Content-Based Image Retrieval, 29–48. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6759-4_2.

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Chakraborty, Sayan, Prasenjit Kumar Patra, Nilanjan Dey, and Amira S. Ashour. "Content-Based Image Retrieval Techniques." In Mining Multimedia Documents, 117–32. Boca Raton : CRC Press, [2017]: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21638-9.

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Chakraborty, Sayan, Prasenjit Kumar Patra, Nilanjan Dey, and Amira S. Ashour. "Content-Based Image Retrieval Techniques." In Mining Multimedia Documents, 117–32. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315399744-10.

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Safar, Maytham H., and Cyrus Shahabi. "Alternative Image Description Techniques." In Shape Analysis and Retrieval of Multimedia Objects, 19–27. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0349-1_4.

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Rui, Yong, and Thomas S. Huang. "Relevance Feedback Techniques in Image Retrieval." In Principles of Visual Information Retrieval, 219–58. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-3702-3_9.

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Pu, Pearl, and Zoran Pečenović. "Dynamic Overview Techniques for Image Retrieval." In Eurographics, 43–52. Vienna: Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-6783-0_5.

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Stehling, Renato O., Mario A. Nascimento, and Alexandre X. Falcão. "Techniques for Color-Based Image Retrieval." In Multimedia Mining, 61–82. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1141-0_5.

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9

Zhang, Yu-Jin. "Content-Based Visual Information Retrieval." In A Selection of Image Understanding Techniques, 243–82. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003362388-7.

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10

Ngo, C. W., T. C. Pong, and R. T. Chin. "Exploiting image indexing techniques in DCT domain." In Multimedia Information Analysis and Retrieval, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0016499.

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Тези доповідей конференцій з теми "IMAGE RETRIEVAL TECHNIQUES"

1

Bird, C. L., and P. J. Elliott. "Search Techniques Within a Multiple Database Environment." In Challenge of Image Retrieval. BCS Learning & Development, 1998. http://dx.doi.org/10.14236/ewic/cir1998.3.

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2

D'britto, Mamta Peter, and Abhijit R. Joshi. "Analysis of image retrieval techniques." In 2017 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2017. http://dx.doi.org/10.1109/iccci.2017.8117740.

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3

Ning, Xiaogang, Deren Li, and Weizhi Ye. "Content-based remote sensing image retrieval." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654549.

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4

Kondekar, Vipul, Vijaykumar Kolkure, Girish Sodal, and Jagdish Mudegaonkar. "Image retrieval techniques based on image features." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742145.

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5

Ruberto, Cecilia Di, and Andrea Morgera. "Moment-Based Techniques for Image Retrieval." In 2008 19th International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2008. http://dx.doi.org/10.1109/dexa.2008.73.

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6

Lu, Lizhen, Renyi Liu, and Nan Liu. "GIS semantics-based approach of remote sensing image retrieval." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655219.

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7

Wang, Zhao-hui, and Sheng-rong Gong. "MAIRS: a content-based multi-agent image retrieval system." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655288.

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8

"TEXTURE BASED IMAGE INDEXING AND RETRIEVAL." In Mathematical and Linguistic Techniques for Image Mining. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002065801770181.

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9

Hussain, Chesti Altaff, D. Venkata Rao, and S. Aruna Masthani. "Image retrieval using saliency content." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7754804.

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10

Ito, M., M. Koike, K. Ikeda, S. Hidaka, and T. Aoki. "Examination of Image Retrieval System Using Subjective Image Information." In 6th Asia-Pacific Symposium on Information and Telecommunication Techniques. IEEE, 2005. http://dx.doi.org/10.1109/apsitt.2005.203641.

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Звіти організацій з теми "IMAGE RETRIEVAL TECHNIQUES"

1

Hoch, Brendon, and Samantha Cook. A 10-Year monthly climatology of wind direction : case-study assessment. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46912.

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Анотація:
A 10-year monthly climatology of wind direction in compass degrees is developed utilizing datasets from the National Oceanic Atmospheric Administration, Climate Forecast System. Data retrieval methodologies, numerical techniques, and scientific analysis packages to develop the climatology are explored. The report describes the transformation of input data in Gridded Binary format to the Geographic Tagged Image File Format to support geospatial analyses. The specific data sources, software tools, and data-verification techniques are outlined.
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2

Rigotti, Christophe, and Mohand-Saïd Hacid. Representing and Reasoning on Conceptual Queries Over Image Databases. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.89.

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Анотація:
The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient.
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3

Rigotti, Christophe, and Mohand-Saïd Hacid. Representing and Reasoning on Conceptual Queries Over Image Databases. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.89.

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Анотація:
The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient.
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4

Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Анотація:
Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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