Iterative Incremental Shot Clustering Algorithm by Haar Wavelets

J. Liao, G. Wang, B. Zhang, and Miao Li (PRC)


Multimedia databases, shot clustering algorithm, multi resolution analysis, stop criterion


Video shot clustering is the basis of other high-level re search of multimedia databases applications. This article proposes a novel and efficient shot clustering algorithm for videos by applying the multi-resolution analysis of Haar wavelets which is called MLHC(Multi-Level Hierarchical Clustering). Corresponding to the reconstruction proce dures of Haar wavelets, MLHC is designed as a multi-level algorithm. When the algorithm runs to further levels, the clustering results are increasingly credible and precise. Af ter the clustering results achieve a stable status, MLHC stops automatically. Thus it’s an iterative incremental clus tering algorithm. Each level of MLHC is an indepen dent hierarchical clustering algorithm which resolves the dilemma of choosing proper initial cluster centers for most existing shot clustering algorithms. For each hierarchical level of MLHC, a novel stop criterion is designed to stop the iterative merging procedures and terminates MLHC on this level. By this stop criterion, the clustering results can be obtained automatically without any parameters and the number of clusters can also be estimated at the same time. The theoretical analysis and the extensive experiments wit ness the efficiency and effectiveness of our proposals.

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