Abstract
Recently, web video categorization has been an ever interesting research with the popularity of web videos. Clustering ensemble has become a good alternative for categorization. Semi-supervised clustering ensemble has shown a better performance since it may incorporate the known prior knowledge, e.g., pairwise constraints. In this paper, we propose a Semi-supervised Cluster-based Similarity Partitioning Algorithm (SS-CSPA) to categorize the videos containing textual data provided by their up-loaders. The feature of this algorithm is the introduction of an unsupervised learning, consensus between clustering and additional support of pairwise constraints to formulate semi-supervised clustering ensemble paradigm. Experimental results on the real-world web videos show that the proposed algorithm outperforms existing methods for categorization of web videos.
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Mahmood, A., Li, T., Yang, Y., Wang, H., Afzal, M. (2013). Semi-supervised Clustering Ensemble for Web Video Categorization. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_17
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DOI: https://doi.org/10.1007/978-3-642-38067-9_17
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