Abstract
Cast indexing is a very important application for content-based video browsing and retrieval, since the characters in feature-length films and TV series are always the major focus of interest to the audience. By cast indexing, we can discover the main cast list from long videos and further retrieve the characters of interest and their relevant shots for efficient browsing. This paper proposes a novel cast indexing approach based on hierarchical clustering, semi-supervised learning and linear discriminant analysis of the facial images appearing in the video sequence. The method first extracts local SIFT features from detected frontal faces of each shot, and then utilizes hierarchical clustering and Relevant Component Analysis (RCA) to discover main cast. Furthermore, according to the user’s feedback, we project all the face images to a set of the most discriminant axes learned by Linear Discriminant Analysis (LDA) to facilitate the retrieval of relevant shots of specified person. Extensive experimental results on movie and TV series demonstrate that the proposed approach can efficiently discover the main characters in such videos and retrieve their associated shots.
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
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 315–333 (2004)
Song, G., Ai, H., Xu, G.: Hierarchical direct appearance model for elastic labeled graph localization. In: Proc of SPIE, pp. 139–144 (2003)
Kittler, J., Hilton, A., Hamouz, M., Illingworth, J.: 3D assisted face recognition: a survey of 3D imaging, modelling and recognition approaches. In: Proc. of IEEE CVPR, pp. 144–144 (2005)
Sivic, J., Everingham, M., Zisserman, A.: Person spotting: video shot retrieval for face sets. In: Proc. of IEEE CIVR, pp. 226–236 (2005)
Yuan, J.H., Zheng, W.J., Chen, L., et al.: Tsinghua University a TRECVID 2004: shot boundary detection and high-level feature extraction. In: NIST workshop of TRECVID (2004)
Arandjelovic, G., Shakhnarovich, J., Fisher, R.: Cipolla, and T. Darrell: Face recognition with image sets using manifold density divergence. In: Proc. of IEEE CVPR, pp. 581–588 (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of IEEE CIVR, pp. 511–518 (2001)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, Chichester (2000)
Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35, 399–458 (2003)
BarHillel, T., Hertz, M., Shental, D.: Weinshall: Learning distance fucntions using equivalence relations. In: Proc. of ICML (2003)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on PAMI 19, 711–720 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fan, W., Wang, T., Bouguet, J., Hu, W., Zhang, Y., Yeung, DY. (2006). Semi-supervised Cast Indexing for Feature-Length Films. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_61
Download citation
DOI: https://doi.org/10.1007/978-3-540-69423-6_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69421-2
Online ISBN: 978-3-540-69423-6
eBook Packages: Computer ScienceComputer Science (R0)