Abstract
In this work, we present a novel method to detect violent shots in movies. The detection process is split into two views—the audio and video views. From the audio-view, a weakly-supervised method is exploited to improve the classification performance. And from the video-view, we use a classifier to detect violent shots. Finally, the auditory and visual classifiers are combined in a co-training way. The experimental results on several movies with violent contents preliminarily show the effectiveness of our method.
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
Swanson, M.D., Zhu, B., Tewfik, A.H.: Data Hiding for Video-in-Video. In: IEEE International Conference on Image Processing, vol. 2, pp. 676–679 (1997)
Vasconcelos, N., Lippman, A.: Towards Semantically Meaningful Feature Spaces for The Characterization of Video Content. In: Proceedings of International Conference on Image Processing, 1997, vol. 1, pp. 25–28 (1997)
Datta, A., Shah, M., Lobo, N.D.V.: Person-on-Person Violence Detection in Video Data. In: IEEE International Conference on Pattern Recognition, pp. 433–438 (2002)
Nam, J., Alghoniemy, M., Tewfik, A.H.: Audio-Visual Content-Based Violent Scene Characterization. In: IEEE International Conference on Image Processing, vol. 1, pp. 353–357 (1998)
Cheng, W., Chu, W., Wu, J.: Semantic Context Detection Based on Hierarchical Audio models. In: Proceedings of the 5th ACM SIGMM international Workshop on Multimedia information Retrieval, pp. 109–115 (2003)
Cai, L.J., Hofmann, T.: Text Categorization by Boosting Automatically Extracted Concepts. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and Development, pp. 182–189 (2003)
Akita, Y., Kawahara, K.: Language Modeling Adaptation Based on PLSA of Topics and Speakers. In: 8th International Conference on Spoken Language Processing, pp. 1045–1048 (2004)
Wold, E., Blum, T., Keislar, D., Wheaten, J.: Content-Based Classification, Search, and Retrieval of Audio, Multimedia, IEEE  3, 27–36 (1996)
Cai, R., Lu, L., Hanjalic, A., Zhang, H.J., Cai, L.H.: A Flexible Framework for Key Audio Effects Detection and Auditory Context Inference. IEEE Transaction on Audio, Speech and Language Processing 14, 1026–1039 (2006)
Wang, Y., Liu, Z., Huang, J.C.: Multimedia Content Analysis Using Both Audio and Visual Clues. IEEE Signal Processing Magazine 17, 12–36 (2000)
Sarkar, A.: Applying Co-Training Methods to Statistical Parsing. In: Proceedings of the 2nd Annual Meeting of the NAACL (2001)
Ng, V., Cardie, C.: Weakly Supervised Natural Language Learning Without Redundant Views. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 94–101 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, J., Wang, W. (2009). Weakly-Supervised Violence Detection in Movies with Audio and Video Based Co-training. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_84
Download citation
DOI: https://doi.org/10.1007/978-3-642-10467-1_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10466-4
Online ISBN: 978-3-642-10467-1
eBook Packages: Computer ScienceComputer Science (R0)