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Relevant bibliographies by topics / IMAGE RETRIEVAL TECHNIQUES

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Academic literature on the topic 'IMAGE RETRIEVAL TECHNIQUES'

Author: Grafiati

Published: 11 September 2023

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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 (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 tech

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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 (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 wa

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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 (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 (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 retr

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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 (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 te

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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 (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 ka

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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 (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

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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 (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 o

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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 (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 (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 reflecti

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Dissertations / Theses on the topic "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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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (p. 81-82).<br>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 th

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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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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 retrie

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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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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.<br>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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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 dist

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Books on the topic "IMAGE RETRIEVAL TECHNIQUES"

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

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

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

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

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

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

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

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

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

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Visual information retrieval. Morgan Kaufmann Publishers, 1999.

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Book chapters on the topic "IMAGE RETRIEVAL TECHNIQUES"

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Safar, Maytham H., and Cyrus Shahabi. "Image Description Techniques." In Shape Analysis and Retrieval of Multimedia Objects. 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. 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. 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. 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. 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. 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. 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. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1141-0_5.

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

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

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Conference papers on the topic "IMAGE RETRIEVAL TECHNIQUES"

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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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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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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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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. ACM, 2010. http://dx.doi.org/10.1145/1741906.1742145.

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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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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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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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"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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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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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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Reports on the topic "IMAGE RETRIEVAL TECHNIQUES"

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

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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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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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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) Featu

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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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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) Featu

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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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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, v

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