Abstract
Movie affective content analysis attracts increasing research efforts since affective content not only affect users attentions but also locate movie highlights. However, affective content retrieval is still a challenging task due to the limitation of affective features in movies. Scripts provide direct access to the movie content and represent affective aspects of the movie. In this paper, we utilize scripts as an important clue to retrieve video affective content. The proposed approach includes two main steps. Firstly, affective script partitions are extracted by detecting emotional words. Secondly, affective partitions are validated by using visual and auditory features. The results are encouraging and compared with the manually labelled ground truth.
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
Kang, H.B.: Affective content detection using hmms. In: Proc. of ACM International Conference on Multimedia, pp. 259–262 (2003)
Hanjalic, A., Xu, L.Q.: Affective video content representation and modeling. IEEE Transaction on Multimedia 7, 143–154 (2005)
Wang, H.L., Cheong, L.F.: Affective understanding in film. IEEE Transactions on Circuits and Systems for Video Technology 16, 689–704 (2006)
Rasheed, Z., Sheikh, Y., Shah, M.: On the use of computable features for film classification. IEEE Transactions on Circuits and Systems for Video Technology 15, 52–64 (2005)
Xu, M., Jin, J.S., Luo, S., Duan, L.: Hierarchical movie affective content analysis based on arousal and valence features. In: Proc. of ACM International Conference on Multimedia, pp. 677–680 (2008)
Zhang, S., Tian, Q., Huang, Q., Gao, W., Li, S.: Utilizing affective analysis for efficient movie browsing. In: Proc. of IEEE International Conference on Image Processing, ICIP (2009)
Soleymani, M., Kierkels, J.J.M., Chanel, G., Pun, T.: A bayesian framework for video affective representation. In: Proc. of the International Conference on Affective Computing and Intelligent Interaction (2009)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: Proc. of International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 119–126 (2003)
Xu, C., Zhang, Y., Zhu, G., Rui, Y., Lu, H., Huang, Q.: Using webcast text for semantic event detection in broadcast sports video. IEEE Transactions on Multimedia 10, 1342–1355 (2008)
Hanjalic, A.: Shot-boundary detection: unravelled and resolved. IEEE Transactions on Circuits and Systems for Video Technology 12(2), 90–105 (2002)
Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using linear algebra for intelligent information retrieval. SIAM Review 37, 301–328 (1995)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Berry, M.W., Drmavc, Z., Jessup, E.R.: Matrices, vector spaces, and information retrieval. SIAM Review 41, 335–362 (1999)
Drummond, T.: Vocabulary of Emotions, http://www.sba.pdx.edu/.../FeelingsList.pdf
Steve: Feeling Words: Words Describing Feelings, http://eqi.org/fw.htm
Kovecses, Z.: Metaphor and Emotion Language, Culture, and Body in Human Feeling. Cambridge University Press, Cambridge (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, M., He, X., Jin, J.S., Peng, Y., Xu, C., Guo, W. (2010). Using Scripts for Affective Content Retrieval. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-15696-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15695-3
Online ISBN: 978-3-642-15696-0
eBook Packages: Computer ScienceComputer Science (R0)