Relevant bibliographies by topics / Content-Based Image Retrieval (CBIR)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Content-Based Image Retrieval (CBIR)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Content-Based Image Retrieval (CBIR)"

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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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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user appli
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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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Anuradha, Shitole1 and Uma Godase2. "Survey on Content Based Image Retrieval." International Journal of Computer-Aided Technologies (IJCAx) 01, dec (2014): 01–09. https://doi.org/10.5281/zenodo.1450266.

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Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques
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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 (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 feat
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Belattar, Khadidja, Sihem Mostefai, and Amer Draa. "Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis." Journal of Information Technology Research 10, no. 1 (2017): 85–108. http://dx.doi.org/10.4018/jitr.2017010106.

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The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of
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Kaur, Bhupinder. "A Deep Learning Approach for Content-Based Image Retrieval." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50977.

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Content-Based Image Retrieval (CBIR) aims to retrieve relevant images based on visual content rather than metadata, addressing the limitations of traditional retrieval methods. This study proposes a deep learning-based CBIR system utilizing Convolutional Neural Networks (CNNs) for automatic feature extraction. Leveraging the CIFAR-10 dataset, the system is evaluated against traditional handcrafted methods such as color histograms and color moments. Various retrieval paradigms image-based, text-based, sketch-based, and conceptual layout are analyzed for performance comparison. Experimental resu
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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 (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, fe
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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.

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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop diffe
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Navdeep, Kaur* Jasdeep Singh Mann2. "CONTENT BASED IMAGE RETRIEVAL USING MULTI SVM AND COLOR AND TEXTURE COMBINATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 79–86. https://doi.org/10.5281/zenodo.2595823.

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The dramatic rise in the sizes of images databases has stirred the development of effective and efficient retrieval systems. The development of these systems started with retrieving images using textual connotations but later introduced image retrieval based on content. This came to be known as Content Based Image Retrieval or CBIR. Systems using CBIR retrieve images based on visual features such as texture, color and shape, as opposed to depending on image descriptions or textual indexing. In the proposed work we will use various types of image features like color, texture, shape, energy, amp
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Dissertations / Theses on the topic "Content-Based Image Retrieval (CBIR)"

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Macena, Júnior Elias Borges. "Aplicação de técnicas de content-based image retrieval (CBIR) em imagens radiográficas." Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/6405.

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Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-10-17T13:34:17Z No. of bitstreams: 2 Dissertação - Elias Borges Macena Junior - 2016.pdf: 9321304 bytes, checksum: ca477b8a1eeb56b690f41c443b0ca638 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2016-10-17T17:17:50Z (GMT) No. of bitstreams: 2 Dissertação - Elias Borges Macena Junior - 2016.pdf: 9321304 bytes, checksum: ca477b8a1eeb56b690f41c443b0ca638 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>
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Larsson, Jimmy. "Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574.

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With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how much the precision and recall of search queries is improved by using a word taxonomy on traditional Text-Based Image Search and Content-Based Image Search. By applying a word taxonomy to different data sources, a strong keyword filter and a keyword extender were implemented and tested. The results show that depending on the implementation, the precision or the recall can be increased. By using a similar approach on real life implementations, it is possible to force
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Voulgaris, Georgios. "Techniques for content-based image characterization in wavelets domain." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/techniques-for-contentbased-image-characterization-in-wavelets-domain(14c72275-a91e-4ba7-ada8-bdaee55de194).html.

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This thesis documents the research which has led to the design of a number of techniques aiming to improve the performance of content-based image retrieval (CBIR) systems in wavelets domain using texture analysis. Attention was drawn on CBIR in transform domain and in particular wavelets because of the excellent characteristics for compression and texture extraction applications and the wide adoption in both the research community and the industry. The issue of performance is addressed in terms of accuracy and speed. The rationale for this research builds upon the conclusion that CBIR has not
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Banda, Juan. "Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis." Diss., Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/banda/BandaJ0511.pdf.

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With the launch of NASA's Solar Dynamics Observatory mission, a whole new age of high-quality solar image analysis was started. With the generation of over 1.5 Terabytes of solar images, per day, that are ten times higher resolution than high-definition television, the task of analyzing them by scientists by hand is simply impossible. The storage of all these images becomes a second problem of importance due to the fact that there is only one full copy of this repository in the world, therefore an alternate and compressed representation of these images is of vital importance. Current automated
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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The
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Weng, Zumao. "Distributed knowledge based image contents retrieval and exploration." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370088.

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Makovoz, Gennadiy. "Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/227.

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The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic similarity. The effectiveness of the LSA retrieval was evaluated based on precision, recall, and F-score. This work extended the application of LSA to high-resolution CT radiology images. The images were chosen for their unique characteristics and their importa
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Viet, Tran Linh. "Efficient Image Retrieval with Statistical Color Descriptors." Doctoral thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5002.

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Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the (dis-)similarity between images based on the descriptions of their color distributions in order to quickly find relevant images. The development and investigation of statistical methods for robust representations of such distributions, the construction of distance measures between them and their applications in efficient retrieval
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Filardi, Ana Lúcia. "Análise e avaliação de técnicas de interação humano-computador para sistemas de recuperação de imagens por conteúdo baseadas em estudo de caso." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06122007-123935/.

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A recuperação de imagens baseada em conteúdo, amplamente conhecida como CBIR (do inglês Content-Based Image Retrieval), é um ramo da área da computação que vem crescendo muito nos últimos anos e vem contribuindo com novos desafios. Sistemas que utilizam tais técnicas propiciam o armazenamento e manipulação de grandes volumes de dados e imagens e processam operações de consultas de imagens a partir de características visuais extraídas automaticamente por meio de métodos computacionais. Esses sistemas devem prover uma interface de usuário visando uma interação fácil, natural e atraente entre o u
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Henrysson, Jennie, Kristina Johansson, and Charlotte Juhlin. "Vad säger bilden? : En utvärdering av återvinningseffektiviteten i ImBrowse." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18375.

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The aim of this master thesis is to evaluate the performance of the content-based image retrieval system ImBrowse from a semantic point of view. Evaluation of retrieval performance is a problem in content-based image retrieval (CBIR). There are many different methods for measuring the performance of content-based image retrieval systems, but no common way for performing the evaluation. The main focus is on image retrieval regarding the extraction of the visual features in the image, from three semantic levels. The thesis tries to elucidate the semantic gap, which is the problem when the system
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Books on the topic "Content-Based Image Retrieval (CBIR)"

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Meduri, Ramalingamurthy. Worm-web search: A content-based image retrieval (CBIR) system for the parasite image collection in the Harold W. Manter Laboratory of Parasitology, University of Nebraska State Museum. Texas Tech University, 2008.

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Eakins, J. P. Content-based image retrieval. JISC Technology Applications Pogramme, 1999.

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Tyagi, Vipin. Content-Based Image Retrieval. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6759-4.

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Eakins, John. Content-based image retrieval. Joint Information Systems Committee, 1999.

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Marques, Oge, and Borko Furht. Content-Based Image and Video Retrieval. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0987-5.

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Marques, Oge. Content-based image and video retrieval. Kluwer Academic Publishers, 2002.

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1953-, Deb Sagarmay, ed. Multimedia systems and content-based image retrieval. Idea Group Publishing, 2004.

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Kushki, Azadeh. An interactive framework for content-based image retrieval. National Library of Canada, 2003.

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1965-, Ma Zongmin, ed. Artificial intelligence for maximizing content based image retrieval. Information Science Reference, 2009.

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Veltkamp, Remco C., Hans Burkhardt, and Hans-Peter Kriegel, eds. State-of-the-Art in Content-Based Image and Video Retrieval. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9664-0.

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Book chapters on the topic "Content-Based Image Retrieval (CBIR)"

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Agrawal, Deepti, Apurva Agarwal, and Dilip Kumar Sharma. "Content-Based Image Retrieval (CBIR): A Review." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8892-8_33.

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Estrela, Vania V., Abdullah Ayub Khan, Aftab Ahmed Shaikh, et al. "Some Issues Regarding Content-Based Image Retrieval (CBIR) for Remote Healthcare Theradiagnosis." In Intelligent Healthcare Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003196822-7.

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Chandana, P., P. Srinivas Rao, C. H. Satyanarayana, Y. Srinivas, and A. Gauthami Latha. "An Efficient Content-Based Image Retrieval (CBIR) Using GLCM for Feature Extraction." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3779-5_4.

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Prasanthi, B., P. Suresh, and D. Vasumathi. "Index-Based Image Retrieval-Analyzed Methodologies in CBIR." In Lecture Notes in Networks and Systems. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3935-5_24.

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Haruechaiyasak, Choochart, and Chaianun Damrongrat. "Improving Social Tag-Based Image Retrieval with CBIR Technique." In The Role of Digital Libraries in a Time of Global Change. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13654-2_26.

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Ramamurthy, B., K. R. Chandran, V. R. Meenakshi, and V. Shilpa. "CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images." In Eco-friendly Computing and Communication Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32112-2_16.

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Zhang, Yu-Jin. "Content-Based Retrieval." In Handbook of Image Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5873-3_44.

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Furht, Borko, Stephen W. Smoliar, and HongJiang Zhang. "Content-Based Image Retrieval." In Video and Image Processing in Multimedia Systems. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2277-5_11.

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da Silva Torres, Ricardo, Nádia P. Kozievitch, Uma Murthy, and Alexandre X. Falcão. "Content-Based Image Retrieval." In Digital Library Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-02284-5_1.

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Wang, Xiaoling, and Kanglin Xie. "Fuzzy Logic-Based Image Retrieval." In Content Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30483-8_29.

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Conference papers on the topic "Content-Based Image Retrieval (CBIR)"

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Li, Sinian, Doruk Barokas Profeta, and Justin Dauwels. "MoReSo: A DNN Framework Expediting Content-Based Video Image Retrieval (CBVIR)." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715173.

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Ahmed Hasan, Soran, and Gullanar M Hadi. "Review about SIFT and Local Feature Extraction in Content Based Image Retrieval." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1533.

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As internet technology expands and the widespread use of digital devices, Content Based Image Retrieval CBIR has seen rapid development and application across a range of areas in computer vision and artificial intelligence Today, it's possible to retrieve related images efficiently and effectively from large scale databases using just an input image, In the last decade, there has been a significant push towards developing new CBIR theories and models, resulting in the establishment of many effective CBIR algorithms. CBIR is a crucial tool for locating images within a large dataset that share s
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J, Anto Germin Sweeta, and Sivagami B. "Review on Topical Content-Based Image Retrieval Systems in the Medical Realm." In 7th International Conference on Recent Innovations in Computer and Communication (ICRICC 23). International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/odbb5333/icricc23p16.

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Content Based Image Retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the decades. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that are more than simple text based queries or requests based on matching exact database fields. Content based Image Retrieval (CBIR) aids radiologist to identify similar medical images in recalling previous cases during diagnosis. Although several algorithms have been introduced to extract
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Khan, Sumaira Muhammad Hayat, Ayyaz Hussain, and Imad Fakhri Taha Alshaikhli. "Comparative Study on Content-Based Image Retrieval (CBIR)." In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2012. http://dx.doi.org/10.1109/acsat.2012.40.

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Valem, Lucas Pascotti, and Daniel Carlos Guimarães Pedronette. "Unsupervised Selective Rank Fusion on Content-Based Image Retrieval." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8303.

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Mainly due to the evolution of technologies to store and share images, the growth of image collections have been remarkable for years. Therefore, developing effective methods to index and retrieve such extensive available visual information is indispensable. The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual descriptors and machine learning methods. Despite the relevant advances in the area, mainly driven by deep learning technologies, accurately computing the similarity bet
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Fachrurrozi, Muhammad, Erwin, Saparudin, and Mardiana. "Multi-object face recognition using Content Based Image Retrieval (CBIR)." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167132.

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Sardey, M. P., and M. P. Dale. "Interactive retrieval relevance feedback approach - a tool for content based image retrieval (CBIR)." In National Conference on Signal and Image Processing Applications. IET, 2009. http://dx.doi.org/10.1049/ic.2009.0166.

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Valem, Lucas Pascotti, and Daniel Carlos Guimarães Pedronette. "Unsupervised Selective Rank Fusion for Content-based Image Retrieval." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2020.11370.

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The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiv
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Wankhede, Vrushali A., and Prakash S. Mohod. "Content-based image retrieval from videos using CBIR and ABIR algorithm." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342767.

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Deekshatulu, B. L. "Learning Semantics in Content Based Image Retrieval (CBIR) - A Brief Review." In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems (VCON). IEEE, 2010. http://dx.doi.org/10.1109/vcon.2010.22.

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Reports on the topic "Content-Based Image Retrieval (CBIR)"

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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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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.

Full text
Abstract:
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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3

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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Abstract:
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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