Multimedia understanding could be a quickrising analysis. Advances in multimedia system understanding area unit connected on to advances in signal knowledge base analysis space. there`s tremendous potential for effective use of multimedia system content through intelligent process, pc vision, pattern recognition, multimedia system databases, and sensible sensors. In reality, each content and context info area unit made sources of knowledge for mining, and therefore the full power of mining and process algorithms is completed solely with the utilization of a mixture of the 2. As digital libraries of pictures area unit speedily growing in size, content primarily based image retrieval has been spotlighted in many fields. During this paper we tend to make a case for content and context primarily based multimedia system retrieval, state of art techniques supported multimedia system retrieval. Then as a case study we tend to implement content primarily based image retrieval exploitation color feature.

Information retrieval, Multimedia databases, Content and context links, State of art techniques, Content-based image retrieval

[1] Y. Alp Aslandogan and Clement T. Yu ― Techniques and Systems for Image and Video Retrieval‖ IEEE Transactions on knowedge and data engineering, vol. 11, no. 1, January/February 1999
[2] Guo-Jun Qi, Charu Aggarwal, Qi Tian, Heng Ji, and Thomas S. Huang, ―Exploring Context and Content Links in Social Media: A Latent Space Method ‖ IEEE Transactions on Pattern analysis and machine intelligence, vol. 34, no. 5, May 2012
[3] Nabil R. Adam, Rutgers University Aryya Gangopadhyay, University of Maryland, Baltimore County ― Content-Based Retrieval in Digital Libraries ‖ Technical Activities Forum January 1998
[4] Chih-Chin Lai, Member and Ying-Chuan Chen ―A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm‖ IEEE Transactions on Instrumentation and measurement, vol. 60, no. 10, October 2011
[5] Young Deok Chun, Nam Chul Kim and Ick Hoon Jang, ―Content-Based Image Retrieval Using Multiresolution Color and Texture Features‖ IEEE Transactions on multimedia, VOL. 10, NO. 6, OCTOBER 2008
[6] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-Based Image Retrieval at the End of the Early Years,‖ IEEETrans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12,pp. 1349-1380, Dec. 2000.
[7] Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, Fellow, and Vincent S. Tseng ― Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns‖ IEEE Transactions on knowedge and data engineering , vol. 23, no. 3, March2011
[8] D. Eck, P. Lamere, T. Bertin-Mahieux, and S. Green, ―Automatic generation of social tags for music recommendation,‖ in Proc. Advances in Neural Information Processing Systems, 2007.
[9] D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, ―Semantic annotation and retrieval of music and sound effects,‖ IEEE Trans. Audio, Speech, Lang. Process., vol. 16, pp. 467–476, 2008.
[10] Weiming Hu, Senior Member, IEEE, 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 cybernetic—part C: Applications and reviews, vol. 41, no. 6, November 2011
[11] C. H. Hoi, L. S. Wong, and A. Lyu,Chinese university of Hong Kong at TRECVID 2006: ―Shot boundary detection and video search,‖ in Proc. TREC Video Retrieval Eval., 2006.
[12] H. Lu, Y.-P. Tan, X. Xue, and L. Wu, ―Shot boundary detection using unsupervised clustering and hypothesis testing,‖ in Proc. Int. Conf. Commun. Circuits Syst., Jun. 2004, vol. 2, pp. 932–936