Abstract
In this paper we introduce stochastic models that characterize the structure of typical television program genres. We show how video sequences can be represented using discrete-symbol sequences derived from shot features. We then use these sequences to build HMM and hybrid HMM-SCFG models which are used to automatically classify the sequences into genres. In contrast to previous methods for using SCGFs for video processing, we use unsupervised training without an a priori grammar.
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
Taskiran, C., Bouman, C., Delp, E.J.: The ViBE video database system: An update and further studies. In: Proceedings of the SPIE/IS&T Conference on Storage and Retrieval for Media Databases 2000, San Jose, CA, pp. 199–207 (2000)
Adams, B., Dorai, C., Venkatesh, S.: Study of shot length and motion as contributing factors to movie tempo. In: Proceedings of the ACM International Conference on Multimedia, Los Angeles, CA, pp. 353–355 (2000)
Vasconcelos, N., Lippman, A.: Statistical models of video structure for content analysis and characterization. IEEE Transactions in Image Processing 9, 3–19 (2000)
Rissanen, J.: A universal prior for integers and estimation by minimum description length. The Annals of Statistics 11, 417–431 (1983)
Liu, Z., Huang, J., Wang, Y.: Classification of TV programs based on audio information using hidden Markov model. In: IEEE Second Workshop on Multimedia Signal Processing, Redondo Beach, CA, pp. 27–32 (1998)
Alatan, A.A., Akansu, A.N., Wolf, W.: Multi-modal dialog scene detection using hidden Markov models for content-based multimedia indexing. Multimedia Tools and Applications 14, 137–151 (2001)
Brand, M., Kettnaker, V.: Discovery and segmentation of activities in video. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 844–851 (2000)
Xie, L., Chang, S.F., Divakaran, A., Sun, H.: Structure analysis of soccer video with hidden Markov models. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Orlando, Fl (2002)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–285 (1989)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Ivanov, Y.A., Bobick, A.: Recogition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 852–872 (2000)
Moore, D., Essa, I.: Recognizing multitasked activities from video using stochastic context-free grammar. In: Workshop on Models versus Exemplars in Computer Vision in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii (2001)
Lari, K., Young, S.J.: The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language 4, 35–56 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Taskiran, C.M., Pollak, I., Bouman, C.A., Delp, E.J. (2003). Stochastic Models of Video Structure for Program Genre Detection. In: GarcÃa, N., Salgado, L., MartÃnez, J.M. (eds) Visual Content Processing and Representation. VLBV 2003. Lecture Notes in Computer Science, vol 2849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39798-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-39798-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20081-9
Online ISBN: 978-3-540-39798-4
eBook Packages: Springer Book Archive