Abstract
Automatic categorization of video shots is the first and necessary step for organizing a long video stream into high-level scenes. However, existing techniques on video shot categorization still suffer from the problem of semantic gap between low-level audio-visual features and high-level semantic concepts. To bridge the gap, current researchers have been making efforts on the characterizations of: (1) spatio-temporal coherence among shots, and (2) bipartite correlation between descriptive features and shot categories. In the most recent works, spectral clustering methods and information-theoretic co-clustering (ITCC) have been actively studied and used to solve the above two issues, respectively. In this paper, we investigate the effectiveness of the two algorithms on video shot categorization. The comparison is examined in terms of estimating number of clusters and classification accuracies, where the K-means clustering algorithm is used as the benchmark. Experiments on 4-h sports videos show that both algorithms perform better than K-means. While the ITCC algorithm has advantages in estimating the number of clusters, the spectral clustering is better concerning the classification accuracy.
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Wang, P., Liu, ZQ. & Yang, SQ. Investigation on unsupervised clustering algorithms for video shot categorization. Soft Comput 11, 355–360 (2007). https://doi.org/10.1007/s00500-006-0089-z
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DOI: https://doi.org/10.1007/s00500-006-0089-z