Abstract
Content-based video indexing and retrieval algorithms are presented in this paper that aim at temporally indexing a video sequence according to actors. Our system splits a video into a sequence of a few representative frames. We use color information and then SGLD matrix on the representative frames for face region detection. Detected faces are used to build a face database. We construct eigen faces applying PCA on the faces in the face database for extracting important features. Extracted features are then used in MPM for identifying the input face from the training faces. Experimental result shows that our approach can correctly recognize 95.3% and 90.84% of the faces from the AT&T face database and video sequence respectively.
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References
Rosenfeld, A., et al.: Video Mining. Kluwer Academic Publishers, Dordrecht (2003)
Yeung, M., et al.: Segmentation of Video by Clustering and Graph Analysis. Computer Vision and Image Understanding 71(1), 94–109 (1998)
Wang, H., et al.: A Highly Efficient system for Automatic Face Region Detection in MPEG Video. IEEE Transactions on Circuit and Systems for Video Technology 7(4), 615–628 (1997)
Li, Y., et al.: Video Content Analysis using Multimodal Information. Kluwer Academic Publishers, Dordrecht (2003)
Yang, M.-H., et al.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)
Dai, Y., Nakano, Y.: Face-Texture Model Based On SGLD And Its Application in Face Detection in a Color Scene. Pattern Recognition 29(6), 1007–1017 (1996)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Chichester (2001)
Breiman, Friedman, et al.: Classification And Regression Trees. CRC Press, Boca Raton (1998)
Zhang, J., Yong, Y., Lades, M.: Face recognition: eigenface, elastic matching, and neural nets. Proc. of the IEEE 85, 1423–1435 (1997)
Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)
Phillips: Support vector machines applied to face recognition. In: Proc. Advances in Neural Information Processing Systems (1998)
Lankriet, G.R.G., El Ghaoui, L., Bhattacharyya, C., Jordan, M.I.: Minimax Probability Machine. In: Proc. Advances in Neural Information Processing Systems (2002)
Lankriet, G.R.G., El Ghaoui, L., Bhattacharyya, C., Jordan, M.I.: A robust minimax approach to classification. Journal of Machine Learning Research 3, 552–582 (2002)
Marshall, W., Olkin, I.: Multivariate chyshev inequalities. Annuals of Mathematcal statistics 31(4), L1001–L1014 (1960)
Popescu, Bertsimas, D.: Optimal inequalities in probability theory: A convex optimization approach. Technical Report TM62, INSEAD, Dept. math. O.R., Cambridge, Mass. (2001)
Lanckriet, G.R.G., El Ghaoui, L., Bhattacharyya, C., Jordan, M.I.: A robust minimax approach to classification. Journal of Machine Learning Research 3, 552–582 (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Islam, M.K., Lee, ST., Baek, JH. (2006). Actor Based Video Indexing and Retrieval Using Visual Information. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_61
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DOI: https://doi.org/10.1007/11881223_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
Online ISBN: 978-3-540-45909-5
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