multimodal Social networks Video content indexing
Last modified: 2011-09-02
Abstract
Evolution has proven to be a very powerful mechanism in finding good solutions to difficult problems. One of these problems is video content clustering. The unsupervised classification method we propose use Genetic algorithms. We present a genetic clustering algorithm for video sequence key image quantization as a prior process. A fitness function with a smallest number of variables is proposed. It’s based on the objective function reformulated by Bezdek and the one proposed by Frigui and Krishnapuram in their Competitive Agglomeration algorithm. The proposed clustering genetic algorithm allows the initial population solutions to converge to good results in relatively less run-time. In addition, variable chromosome length is used to determine the clusters’ number by including an audio index. This multimodal description allows us to classify the online videos regarding their offensive nature.
References
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[Hu et al, 2008] W. Hu, O.Wu, Z. Chen, Z. Fu, and S. Maybank. “Recognition of Pornographic Web Pages by Classifying Texts and Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), June, pp 1019-2007, 2008
[Rea et al, 2006] N. Rea, G. Lacey, C. Lambe and R. Dahyot (2006), “Multimodal Periodicity Analysis for Illicit Content Detection in Videos” Conference on Visual Media Production, London, pages 106–114.
[Zuo et al, 2008] Haiqiang Zuo, Ou Wu, Weiming Hu, Bo Xu, “Recognition of blue movies by fusion of audio and video”, IEEE International Conference on Multimedia and Expo, pp.37-40 (2008)
[Cees et al, 2009] Cees G. M. Snoek and Marcel Worring “Concept-Based Video Retrieval” Foundations and Trends in Information Retrieval, Vol. 2, No. 4 (2008) 215–322, 2009
D. Martin, C. Fowlkes, D. Tal and J. Malik, “A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics”, ICCV, vol. 2, pp 416-423, 2001
U. Maulik and S. Bandyopadhyay, “Genetic Algorithm Based Clustering Technique”, Pattern Recognition, vol. 33, no. 9, pp. 1455-1465, 2000.