Abstract
We address the issue of automatic video genre retrieval. We propose three categories of content descriptors, extracted at temporal, color and structural level. At temporal level, video content is described with visual rhythm, action content and amount of gradual transitions. Colors are globally described with statistics of color distribution, elementary hues, color properties and relationship. Finally, structural information is extracted at image level and histograms are built to describe contour segments and their relations. The proposed parameters are used to classify 7 common video genres, namely: animated movies/cartoons, commercials, documentaries, movies, music clips, news and sports. Experimental tests using several classification techniques and more than 91 hours of video footage prove the potential of these parameters to the indexing task: despite the similarity in semantic content of several genres, we achieve detection ratios ranging between 80 − 100%.
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References
Smeaton, A.F., Over, P., Kraaij, W.: High-Level Feature Detection from Video in TRECVid: a 5-Year Retrospective of Achievements, Multimedia Content Analysis. In: Theory and Applications, pp. 151–174. Springer, Berlin (2009); ISBN 978-0-387-76567-9
Brezeale, D., Cook, D.J.: Automatic Video Classification: A Survey of the Literature. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(3), 416–430 (2008)
Roach, M.J., Mason, J.S.D.: Video Genre Classification using Dynamics. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Utah, USA, pp. 1557–1560 (2001)
Yuan, X., Lai, W., Mei, T., Hua, X.S., Wu, X.Q., Li, S.: Automatic video genre categorization using hierarchical SVM. In: IEEE International Conference on Image Processing, Atlanta, USA, pp. 2905–2908 (2006)
Montagnuolo, M., Messina, A.: Parallel Neural Networks for Multimodal Video Genre Classification. Multimedia Tools and Applications 41(1), 125–159 (2009)
Ionescu, B., Coquin, D., Lambert, P., Buzuloiu, V.: A Fuzzy Color-Based Approach for Understanding Animated Movies Content in the Indexing Task. Eurasip Journal on Image and Video Processing (2008), doi:10.1155/2008/849625
Rasche, C.: An Approach to the Parameterization of Structure for Fast Categorization. International Journal of Computer Vision 87(3), 337–356 (2010)
Ionescu, B., Pacureanu, A., Lambert, P., Vertan, C.: Highlighting Action Content in Animated Movies. In: IEEE ISSCS - International Symposium on Signals, Circuits and Systems, Iasi, Romania, July 9-10 (2009)
Ionescu, B., Coquin, D., Lambert, P., Buzuloiu, V.: Semantic Characterization of Animation Movies Based on Fuzzy Action and Color Information. In: Marchand-Maillet, S., et al. (eds.) AMR 2006. LNCS, vol. 4398, pp. 119–135. Springer, Heidelberg (2007)
Ionescu, B., Buzuloiu, V., Lambert, P., Coquin, D.: Improved Cut Detection for the Segmentation of Animation Movies. In: IEEE International Conference on Acoustic, Speech and Signal Processing, Toulouse, France (2006)
Fernando, W.A.C., Canagarajah, C.N., Bull, D.R.: Fade and Dissolve Detection in Uncompressed and Compressed Video Sequence. In: IEEE International Conference on Image Processing, Kobe, Japan, pp. 299–303 (1999)
Su, C.-W., Liao, H.-Y.M., Tyan, H.-R., Fan, K.-C., Chen, L.-H.: A Motion-Tolerant Dissolve Detection Algorithm. IEEE Transactions on Multimedia 7(6), 1106–1113 (2005)
Chen, H.W., Kuo, J.-H., Chu, W.-T., Wu, J.-L.: Action Movies Segmentation and Summarization based on Tempo Analysis. In: ACM International Workshop on Multimedia Information Retrieval, New York, pp. 251–258 (2004)
Floyd, R.W., Steinberg, L.: An Adaptive Algorithm for Spatial Gray Scale. In: Proc. SID Int. Symp. Digest of Technical Papers, pp. 36–37 (1975)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005); ISBN 0-12-088407-0
Canny, J.: A Computational Approach To Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
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Ionescu, B.E., Rasche, C., Vertan, C., Lambert, P. (2011). A Contour-Color-Action Approach to Automatic Classification of Several Common Video Genres. In: Detyniecki, M., Knees, P., Nürnberger, A., Schedl, M., Stober, S. (eds) Adaptive Multimedia Retrieval. Context, Exploration, and Fusion. AMR 2010. Lecture Notes in Computer Science, vol 6817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27169-4_6
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DOI: https://doi.org/10.1007/978-3-642-27169-4_6
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