Abstract
We address the problem of classifying scenes from feature films into semantic categories and propose a robust framework for this problem. We propose that the Finite State Machines (FSM) are suitable for detecting and classifying scenes and demonstrate their usage for three types of movie scenes; conversation, suspense and action. Our framework utilizes the structural information of the scenes together with the low and mid-level features. Low level features of video including motion and audio energy and a mid-level feature, face detection, are used in our approach. The transitions of the FSMs are determined by the features of each shot in the scene. Our FSMs have been experimented on over 60 clips and convincing results have been achieved.
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
Adams, B., Dorai, C., Venkatesh, S.: Novel Approach to Determining Tempo and Dramatic Story Sections in Motion Pictures, ICIP (2000)
Rasheed, Z., Shah, M.: Scene Detection In Hollywood Movies and TV Shows. In: IEEE Computer Vision and Pattern Recognition Conference, Madison,Wisconsin, June 16-22 (2003)
Yoshitaka, A., Ishii, T., Hirakawa, M., Ichikawa, T.: Content-Based Retrieval of Video Data by the Grammar of Film. In: IEEE Symposium on Visual Languages (1997)
Lienhart, R., Pfeiffer, S., Effelsberg, W.: Scene Determination Based on Video and Audio Features. In: Proc. IEEE Conf. on Multimedia Computing and Systems, Florence, Italy (1999)
Li, Y., Narayanan, S., Jay Kuo, C.-C.: Movie Content Analysis Indexing, and Skimming. In: Video Mining. ch.5, Kluwer Academic Publishers, Dordrecht (2003)
Viola, P., Jones, M.: Robust Real-Time Object Detection, International Journal of Computer Vision (2001)
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
Zhai, Y., Rasheed, Z., Shah, M. (2004). A Framework for Semantic Classification of Scenes Using Finite State Machines. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-27814-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22539-3
Online ISBN: 978-3-540-27814-6
eBook Packages: Springer Book Archive