Abstract
The prevalence of video sharing websites brings the explosion of web videos and poses a tough challenge to the web video clustering for their indexing. This paper proposes a flexible multi-modal clustering method for web videos. This method achieves web video representation and similarity measurement by integrating the extracted visual features, semantic features and text features of videos to describe a web video more accurately. With the multi-modal combined similarity as input, the affinity propagation algorithm is employed for the clustering procedure. The clustering method is evaluated by experiments conducted on web video dataset and has a better performance than existing methods.
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References
YouTube, http://www.youtube.com
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) (2008)
Liu, S., Zhu, M., Zheng, Q.: Mining Similarities for Clustering Web Video Clips. In: International Conference on Computer Science and Software Engineering, pp. 759–762 (2008)
Jain, A.K.: Data Clustering: 50 Years Beyond K-means. Pattern Recognition Letters 31(8), 651–666 (2010)
Juasiripukdee, P., Wiyartanti, L., Kim, L.: Clustering Search Results of Non-text User Generated Content. In: ICDIM 2010 (2010)
Rasiwasia, N.J., Pereira, C., Coviello, E., Doyle, G., Lanckriet, G.R., Levy, R., Vasconcelos, N.: A New Approach to Cross-modal Multimedia Retrieval. In: ACM Multimedia, pp. 251–260 (2010)
Wei, S., Zhao, Y., Zhu, Z., Liu, N.: Multimodal Fusion for Video Search Reranking. IEEE Transactions on Knowledge and Data Engineering 99(1), 1191–1199 (2009)
Yang, L., Liu, J., Yang, X., Hua, X.S.: Multi-modality Web Video Categorization. In: Multimedia Information Retrieval (MIR), pp. 265–274 (2007)
Hindle, A., Shao, J., Lin, D., Lu, J., Zhang, R.: Clustering Web Video Search Results Based on Integration of Multiple Features. World Wide Web 14(1), 1–21 (2010)
Lowe, D.G.: Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Frey, B.J., Dueck, D.: Clustering by Passing Messages Between Data Points. Science 315(5814), 972–976 (2007)
Cao, J., Zhang, Y.D., Song, Y.C., Chen, Z.N., Zhang, X., Li, J.T.: MCG-WEBV: A Benchmark Dataset for Web Video Analysis. Technical Report, MCG-ICT-CAS-09-001, Institute of Computing Technology (2009)
Kishida, K.: Property of Average Precision and Its Generalization: An Examination of Evaluation Indicator for Information Retrieval Experiments. Nii technical report (2005)
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Huang, H., Lu, Y., Zhang, F., Sun, S. (2013). A Multi-modal Clustering Method for Web Videos. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_21
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DOI: https://doi.org/10.1007/978-3-642-35795-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35794-7
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