A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

References

• Yong Rui and Thomas S. Huang, “Image Retrieval: Current Techniques, Promising Directions, and Open Co-published by Atlantis Press and Taylor & Francis Copyright: the authors A Content Based Video Retrieval Analysis System Issues”, Journal of Visual Communication and Image Representation, Vol. 10, pp. 39–62, 1999.
   Google Scholar
• Elleuch, N.; Ben Ammar, A.; Alimi, A.M. “Semi-automatic soft collaborative annotation for semantic video indexing”, EUROCON - International Conference on Computer as a Tool (EUROCON), pp. 1–6, April 2011.
   Google Scholar
• Jens-Rainer Ohm, F. Bunjamin, W. Liebsch, B. Makai, K. Müller, A. Smolic, D.Zier “A Set of Visual Feature Descriptors and their Combination in a Low-Level Description Scheme”, Signal Processing: Image Communication, Vol. 16, No. 1-2, pp. 157–179, September 2000.
   Google Scholar
• Fong, A.C.M “Content-based video sequence interpretation”, IEEE Transactions on Consumer Electronics, Vol. 47, No. 4, pp.873–879, 2001
   Google Scholar
• Y. Chung, W. Chin, X. Chen, D. Shi, E. Choi, and F. Chen, “Performance analysis of using wavelet transform in content based video retrieval system”, In Proceedings of the WSEAS International Conference on Computer Engineering and Applications, pp. 17–19, 2007.
   Google Scholar
• C. Faloutsos, W. Equitz, M. Flickner, W Niblack, D. Petkovic, and R. Barber, “Efficient and effective querying by image content”, Journal of Intelligent Information Systems, Vol. 3, No.3, pp. 231–262, July 1994.
   Google Scholar
• G. Ahanger and T.D.C. Little, “A Survey of Technologies for Parsing and Indexing Digital Video”, Journal of Visual Communication and Image Representation, Vol. 7, No. 1, pp. 28–43, 1996.
   Google Scholar
• Wallapak Tavanapong and Junyu Zhou, “Shot Clustering Techniques for Story Browsing”, IEEE Transactions on Multimedia, Vol.6, No.4, pp.517–527, Aug 2004
   Google Scholar
• Nida Aslam, Irfanullah, Kok-Keong Loo and Roohullah, “Limitation and Challenges: Image/Video Search & Retrieval”, International Journal of Digital Content Technology and its Applications, Vol.3, No.1, pp.98–102, 03 2009
   Google Scholar
• Dimitrova, “Multimedia Content Analysis and Indexing for Filtering and Retrieval Applications”, Special Issue on Multimedia Informing Technologies, Vol.1, No.4, pp.87–100, 1999
   Google Scholar
• Dagtas, S., Al-Khatib, W. Ghafoor, A. Kashyap, R.L., “Models for motion-based video indexing and retrieval”, IEEE Transactions on Image Processing, Vol. 9, No. 1, pp. 88–101, 2000.
   PubMed Web of Science ®Google Scholar
• Smoliar, S.W, HongJiang Zhang; Siew Lian Koh; Guo Jun Lu “Interacting with digital video”, IEEE Region 10's Ninth Annual International Conference, Vol. 2, pp. 852–856,1994.
   Google Scholar
• Liang-Hua Chen, Kuo-Hao Chin and Hong-Yuan Liao “An Integrated Approach to Video Retrieval”, In proceedings of ACM International Conference Proceeding Series, Vol. 313, pp.49–55, 2008
   Google Scholar
• Xiang Fu and Jie-xian Zeng, “Local Features Based Image Sequence Retrieval”, Journal of Computers, Vol.5, No.7, P.p:987–994, July 2010
   Google Scholar
• Tianming Liu, Hong-Jiang Zhang and Feihu Qi, “A Novel Video Key-Frame-Extraction Algorithm Based on Perceived Motion Energy Model”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.13, No.10, pp.1006–1013, Oct 2003
   Google Scholar
• Alan F. Smeaton, Peter Wilkins, Marcel Worring, Ork de Rooij, Tat-Seng Chua, Huanbo Luan, “Content-based video retrieval: Three example systems from TRECVid”, International Journal of Imaging Systems and Technology, Vol. 18, No. 2, pp. 195–200, 2008.
   Google Scholar
• Yannis S. Avrithis, Anastasios D. Doulamis, Nikolaos D. Doulamis, and Stefanos D. Kollias, “A Stochastic Framework for Optimal Key Frame Extraction from MPEG Video Databases”, Computer Vision and Image Understanding, Vol.75, No.1, pp. 3–24, Aug 1999
   Google Scholar
• Jinqiao Wang, Hanqing Lu, Lingyu Duan and Jesse S. Jin, “Commercial Video Retrieval with Video-based Bag of Words”, In proceedings of Fifth International Conference on Intelligent Multimedia Computing and Networking, pp.1–7, July 2007
   Google Scholar
• Yuxin Peng, Chong-Wah Ngo and Jianguo Xiao, “OM-based video shot retrieval by one-to-one matching”, Multimedia Tools and Applications, Vol.34, pp.249–266, 2007
   Google Scholar
• Sang Hyun Kim and Rae-Hong Park, “An Efficient Algorithm for Video Sequence Matching Using the Modified Hausdorff Distance and the Directed Divergence”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.12, No.7, pp.592–596, July 2002
   Google Scholar
• R. Kanagavalli, K. Duraiswamy, “A Study on Techniques used in Digital Video for Shot Segmentation and Content Based Video Retrieval”, European Journal of Scientific Research, Vol.69, No.3, pp. 370–380, 2012.
   Google Scholar
• Shradha Gupta, Prof. Neetesh Gupta, Dr. Bhupendra Verma, “Performance of SIFT based Video Retrieval”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 6, pp. 2565–2571, June 2011
   Google Scholar
• Weiming Hu, Nianhua Xie, Li Li, Xianglin Zeng, and Stephen Maybank “A Survey on Visual Content-Based Video Indexing and Retrieval”, IEEE Transactions On Systems, Man, And Cybernetics, Vol. 41, No. 6, 2011.
   Google Scholar
• Ionuţ Mironică, Constantin Vertan, Bogdan Ionescu “A Relevance Feedback Approach to Video Genre Retrieval”, IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 327–330, 2011.
   Google Scholar
• BV Patel and BB Meshram “Content Based Video Retrieval Systems”, International Journal of UbiComp (IJU), Vol.3, No.2, pp.13–30, April 2012
   Google Scholar
• Chen, L.H., Chin, K.H. and Liao, H.Y.M. ‘Integration of Color and Motion Features for Video Retrieval,’ International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23, No. 2, pp. 313–329, 2009.
   Google Scholar
• Dyana, A. andDas, S.(2009) ‘Trajectory representation using Gabor features for motion-based video retrieval,’ Pattern Recognition Letters, Vol. 30, No. 10, pp. 877–892.
   Google Scholar
• Ionut Mironică, Constantin Vertan, Bogdan Ionescu “A Relevance Feedback Approach to Video Genre Retrieval”, IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 327–330, 2011.
   Google Scholar
• Jean-François Aujol, Guy Gilboa, Tony Chan, Stanley Osher, “Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection”, International Journal of Computer Vision, Vol. 67, No. 1, April 2006
   Google Scholar
• Priya Rajendran and Shanmugam, “A content-based video retrieval system: video retrieval with extensive features”, International Journal of Multimedia Intelligence and Security, Vol. 2, No.2, pp. 146–171, 2011
   Google Scholar
• Hu and Collomosse, “Motion-sketch based Video Retrieval using a Trellis Levenshtein Distance”, In proceedings of International Conference on Pattern Recognition, 2010
   Google Scholar
• Andre N. Stein and Martial Hebert, “Local Detection of Occlusion Boundaries in Video”, in proceedings of BMVC, pp.407–416, 2006
   Google Scholar
• Piro, Anthoine, Debreuve and Barlaud, “Image retrieval via Kullback-Leibler divergence of patches of multiscale coefficients in the KNN framework”, In proceedings of International Workshop Content Based Multimedia Indexing, pp.230–235, June 2008.
   Google Scholar