Relevant bibliographies by topics / Content-Based Video Retrieval

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

Academic literature on the topic 'Content-Based Video Retrieval'

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

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Journal articles on the topic "Content-Based Video Retrieval"

1

Hamad, Sumaya, Ahmeed Suliman Farhan, and Doaa Yaseen Khudhur. "Content based video retrieval using discrete cosine transform." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 839–45. https://doi.org/10.11591/ijeecs.v21.i2.pp839-845.

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A content based video retrieval (CBVR) framework is built in this paper. One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, check 100 database videos, 5 videos in each category.
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Sheetal, Deepak Patil. "Content Based Image and Video Retrieval: A Compressive Review." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 243–47. https://doi.org/10.35940/ijeat.E2783.0610521.

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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d
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Patel, B. V. "Content Based Video Retrieval." International journal of Multimedia & Its Applications 4, no. 5 (2012): 77–98. http://dx.doi.org/10.5121/ijma.2012.4506.

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K. D, Wagh, and Dr Kharat M. U. "Content Based Video Retrieval." IJARCCE 5, no. 1 (2016): 53–58. http://dx.doi.org/10.17148/ijarcce.2016.5112.

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Waykar, Sanjay B., and C. R. Bharathi. "Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval." Journal of Intelligent Systems 26, no. 3 (2017): 585–99. http://dx.doi.org/10.1515/jisys-2016-0041.

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AbstractDue to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lectu
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Lin, Lin, and Mei-Ling Shyu. "Correlation-Based Ranking for Large-Scale Video Concept Retrieval." International Journal of Multimedia Data Engineering and Management 1, no. 4 (2010): 60–74. http://dx.doi.org/10.4018/jmdem.2010100105.

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Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transacti
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Patil, Sheetal Deepak. "Content Based Image and Video Retrieval A Compressive Review." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 243–47. http://dx.doi.org/10.35940/ijeat.e2783.0610521.

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Abstract:
Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d
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8

Hamad, Sumaya, Ahmeed Suliman Farhan, and Doaa Yaseen Khudhur. "Content based video retrieval using discrete cosine transform." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 839. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp839-845.

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Abstract:
A content based video retrieval (CBVR)framework is built in this paper. One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.
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Chavan, Smita, and Shubhangi Sapkal. "Color Content based Video Retrieval." International Journal of Computer Applications 84, no. 11 (2013): 15–18. http://dx.doi.org/10.5120/14619-2931.

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Patel, B. V. "Content Based Video Retrieval Systems." International Journal of UbiComp 3, no. 2 (2012): 13–30. http://dx.doi.org/10.5121/iju.2012.3202.

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Dissertations / Theses on the topic "Content-Based Video Retrieval"

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Faichney, Jolon. "Content-Based Retrieval of Digital Video." Thesis, Griffith University, 2005. http://hdl.handle.net/10072/365697.

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In the next few years consumers will have access to large amounts of video and image data either created by themselves with digital video and still cameras or by having access to other image and video content electronically. Existing personal computer hardware and software has not been designed to manage large quantities of multimedia content. As a result, research in the area of content-based video retrieval (CBVR) has been underway for the last fifteen years. This research aims to improve CBVR by providing an accurate and reliable shape-colour representation and by providing a new 3D user in
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Zhang, Lelin. "Scalable Content-Based Image and Video Retrieval." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/15439.

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The popularity of the Internet and portable image capturing devices brings in unprecedented amount of images and videos. Content-based visual search provides an important tool for users to consume the ever-growing digital media repositories, and is becoming an increasingly demanding task as never before. In this thesis, we focus on improving the scalability, efficiency and usability of content-based image and video retrieval systems, particularly in dynamic and open environments. Towards our goal, we make four contributions to the research community. First, we propose a scalable approach to ad
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Chen, Juan. "Content-based Digital Video Processing. Digital Videos Segmentation, Retrieval and Interpretation." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4256.

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Recent research approaches in semantics based video content analysis require shot boundary detection as the first step to divide video sequences into sections. Furthermore, with the advances in networking and computing capability, efficient retrieval of multimedia data has become an important issue. Content-based retrieval technologies have been widely implemented to protect intellectual property rights (IPR). In addition, automatic recognition of highlights from videos is a fundamental and challenging problem for content-based indexing and retrieval applications. In this thesis, a paradigm is
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Volkmer, Timo, and timovolkmer@gmx net. "Semantics of Video Shots for Content-based Retrieval." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090220.122213.

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Content-based video retrieval research combines expertise from many different areas, such as signal processing, machine learning, pattern recognition, and computer vision. As video extends into both the spatial and the temporal domain, we require techniques for the temporal decomposition of footage so that specific content can be accessed. This content may then be semantically classified - ideally in an automated process - to enable filtering, browsing, and searching. An important aspect that must be considered is that pictorial representation of information may be interpreted differently by
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Mohanna, Farahnaz. "Content based video database retrieval using shape features." Thesis, University of Surrey, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250764.

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Anjulan, Arasanathan. "Intelligent content based video retrieval based on local region tracks." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445827.

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Wang, Lei. "Content based video retrieval via spatial-temporal information discovery." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/1119.

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Content based video retrieval (CBVR) has been strongly motivated by a variety of realworld applications. Most state-of-the-art CBVR systems are built based on Bag-of-visual- Words (BovW) framework for visual resources representation and access. The framework, however, ignores spatial and temporal information contained in videos, which plays a fundamental role in unveiling semantic meanings. The information includes not only the spatial layout of visual content on a still frame (image), but also temporal changes across the sequential frames. Specially, spatially and temporally co-occurring visu
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Rautiainen, M. (Mika). "Content-based search and browsing in semantic multimedia retrieval." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514283007.

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Abstract Growth in storage capacity has led to large digital video repositories and complicated the discovery of specific information without the laborious manual annotation of data. The research focuses on creating a retrieval system that is ultimately independent of manual work. To retrieve relevant content, the semantic gap between the searcher's information need and the content data has to be overcome using content-based technology. Semantic gap constitutes of two distinct elements: the ambiguity of the true information need and the equivocalness of digital video data. The research probl
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Vemulapalli, Smita. "Audio-video based handwritten mathematical content recognition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45958.

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Recognizing handwritten mathematical content is a challenging problem, and more so when such content appears in classroom videos. However, given the fact that in such videos the handwritten text and the accompanying audio refer to the same content, a combination of video and audio based recognizer has the potential to significantly improve the content recognition accuracy. This dissertation, using a combination of video and audio based recognizers, focuses on improving the recognition accuracy associated with handwritten mathematical content in such videos. Our approach makes use of a video
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Celik, Cigdem. "An Mpeg-7 Video Database System For Content-based Management And Retrieval." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606679/index.pdf.

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A video data model that allows efficient and effective representation and querying of spatio-temporal properties of objects has been previously developed. The data model is focused on the semantic content of video streams. Objects, events, activities performed by objects are the main interests of the model. The model supports fuzzy spatial queries including querying spatial relationships between objects and querying the trajectories of objects. In this thesis, this work is used as a basis for the development of an XML-based video database system. This system is aimed to be compliant with the M
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Books on the topic "Content-Based Video Retrieval"

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Petković, Milan, and Willem Jonker. Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9.

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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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Petkovic̐, Milan. Content-based video retrieval: A database perspective. Kluwer Academic, 2004.

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1962-, Jonker Willem, ed. Content-based video retrieval: A database perspective. Kluwer Academic Publishers, 2004.

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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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Veltkamp, Remco C. State-of-the-Art in Content-Based Image and Video Retrieval. Springer Netherlands, 2001.

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1963-, Veltkamp Remco C., Burkhardt H. 1944-, Kriegel Hans-Peter, and Dagstuhl Seminar on Content-Based Image and Video Retrieval (1999), eds. State-of-the-art in content-based image and video retrieval. Kluwer Academic Publishers, 2001.

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Guo, Ju. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002.

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IEEE Workshop on Content-Based Access of Image and Video Libraries (1998 Santa Barbara, Calif.). IEEE Workshop on Content-based Access of Image and Video Libraries: Proceedings : June 21, 1998, Santa Barabra, California. IEEE Computer Society, 1998.

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Book chapters on the topic "Content-Based Video Retrieval"

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Petković, Milan, and Willem Jonker. "Video Modeling." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_3.

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Petković, Milan, and Willem Jonker. "Introduction." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_1.

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Petković, Milan, and Willem Jonker. "Database Management Systems and Conetent-Based Retrieval." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_2.

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Petković, Milan, and Willem Jonker. "Spatio-Temporal Formalization of Video Events." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_4.

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Petković, Milan, and Willem Jonker. "Stochastic Modeling of Video Events." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_5.

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Petković, Milan. "Cobra: A Prototype of a Video DBMS." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_6.

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Petković, Milan, and Willem Jonker. "Conclusions." In Content-Based Video Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4865-9_7.

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Mihajlović, Vojkan, Milan Petković, Willem Jonker, and Henk Blanken. "Multimodal Content-based Video Retrieval." In Multimedia Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_10.

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Gurrin, Cathal. "Content-Based Video Retrieval." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1027.

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Gurrin, Cathal. "Content-Based Video Retrieval." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_1027-2.

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Conference papers on the topic "Content-Based Video Retrieval"

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Shaik, Farooq, Rajesh Yelchuri, and Jatindra Kumar Dash. "Content Based Video Retrieval with Handcrafted Features." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837294.

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Nguyen, Thao-Nhu, Quang-Linh Tran, Hoang-Bao Le, et al. "VidBasys: A User-Friendly Interactive Video Retrieval System for Novice Users in IVR4B." In 2024 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2024. https://doi.org/10.1109/cbmi62980.2024.10859221.

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Han, Chaowei, Gaofeng Meng, and Chunlei Huo. "SFD: Similar Frame Dataset for Content-Based Video Retrieval." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647446.

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Mu, Xiangming. "Content-based video retrieval." In the 29th annual international ACM SIGIR conference. ACM Press, 2006. http://dx.doi.org/10.1145/1148170.1148314.

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Dimitrovski, Ivica, Suzana Loskovska, Gorgi Kakasevski, and Ivan Chorbev. "Video Content-Based Retrieval System." In EUROCON 2007 - The International Conference on "Computer as a Tool". IEEE, 2007. http://dx.doi.org/10.1109/eurcon.2007.4400527.

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Bolle, R. M. "Content-based digital video retrieval." In International Broadcasting Conference (IBC). IEE, 1997. http://dx.doi.org/10.1049/cp:19971261.

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Vrochidis, Stefanos, Anastasia Moumtzidou, Paul King, Anastasios Dimou, Vasileios Mezaris, and Ioannis Kompatsiaris. "VERGE: A video interactive retrieval engine." In 2010 International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2010. http://dx.doi.org/10.1109/cbmi.2010.5529884.

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Shirahama, Kimiaki, Kazuyuki Otaka, and Kuniaki Uehara. "Content-Based Video Retrieval Using Video Ontology." In Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007). IEEE, 2007. http://dx.doi.org/10.1109/ismw.2007.4475984.

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Shirahama, Kimiaki, Kazuyuki Otaka, and Kuniaki Uehara. "Content-Based Video Retrieval Using Video Ontology." In Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007). IEEE, 2007. http://dx.doi.org/10.1109/ism.workshops.2007.55.

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Foote, Jonathan T. "Content-based retrieval of music and audio." In Voice, Video, and Data Communications, edited by C. C. Jay Kuo, Shih-Fu Chang, and Venkat N. Gudivada. SPIE, 1997. http://dx.doi.org/10.1117/12.290336.

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Reports on the topic "Content-Based Video Retrieval"

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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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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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