Abstract
Human face is a very important semantic cue in video program. Therefore, this paper presents to implement video program content indexing based on Gaussian clustering after face recognition through Support Vector Machine (SVM) and Hidden Markov Model (HMM) hybrid model. The task consists of following steps: first, SVM and HMM hybrid model is used to recognize human face by Independent basis feature of face apparatus; then, the recognized faces are clustered for video content indexing by Mixture Gaussian. From the experiments, the precision of the mixed model for face recognition is 97.8 percent, and the recall is 95.2, which is higher than the complexion model. And the precision of the face clustering indexing is 94.6 percent of the mixed model for compere new program. The indexing result of clustering is famous.
This work is supported by Zhejiang Provincial Natural Science Foundation of China; Grant Number: M503099.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Limin, H., Yuehua, W.: Key techniques of content-based image retrieval in digital library. The Journal of the Library Science in China 28(6), 26–36 (2002)
Limin, H., Yuehua, W.: Key techniques of content-based video retrieval in digital library. The Journal of the Library Science in China 29(2), 52–56 (2003)
Petkovic, M., et al.: Content-based video indexing for the support of digital library search. In: Proceedings 18th International Conference on Data Engineering, pp. 494–495 (2002)
Lin, W.-H., Hauptmann, A.G.: A wearable digital library of personal conversations. In: Proceedings of the ACM International Conference on Digital Libraries, pp. 277–278 (2002)
Lyu, M.R., Yau, E., Sze, S.: A multilingual, multimodal digital video library system. In: Proceedings of the ACM International Conference on Digital Libraries, pp. 145–153 (2002)
Wang, Y., Liu, Z., Huang, J.: Multimedia content analysis using audio and visual information. IEEE Signal Processing Magazine 17(6), 12–36 (2000)
Boser, B.E., Guyon, I. M., et al.: A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp. 144–152 (1992)
Guo, G., Li, S.Z.: Content-based audio classification and retrieval by support vector machines. IEEE Transactions on Neural Networks 14(1), 209–215 (2003)
Cover, T.M.: Geometrical and statistical properties of systems and linear inequalities with applications in pattern recognition. IEEE Trans. on Electronic Computers 19, 326–334 (1965)
Wallhoff, F., et al.: A comparison of discrete and continuous output modeling techniques for a Pseudo-2D Hidden Markov Model face recognition system. In: IEEE International Conference on Image Processing, vol. 2, pp. 685–688 (2001)
Hui, J., Wen, G.: Analysis And Recognition Of Facial Expression Image Sequences Based On Hmm. Acta Automatica Sinica 28(4), 646–650 (2002)
Platt, J.C.: Probabilistic Outputs for Support Vector Machines for Pattern Recognition. In: Fayyad, U. (ed.). Kluwer Academic Publishers, Boston (1999)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39( Series B), 1–38 (1977)
Nefian, A.V., Hayes, M.H.: Hidden Markov Models for Face Recognition. In: International Conference on Acoustics, Speech and Signal Processing, vol. 5, pp. 2721–2724 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wan, Y., Ji, S., Xie, Y., Zhang, X., Xie, P. (2004). Video Program Clustering Indexing Based on Face Recognition Hybrid Model of Hidden Markov Model and Support Vector Machine. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-540-30503-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23942-0
Online ISBN: 978-3-540-30503-3
eBook Packages: Computer ScienceComputer Science (R0)