Sematnic video clustering across sources using bipartite spectral clustering

Data clustering is an important technique for visual data management. Most previous work focuses on clustering video data within single sources. In this paper, we address the problem of clustering across sources, and propose novel spectral clustering algorithms for multi-source clustering problems. Spectral clustering is a new discriminative method realizing clustering by partitioning data graphs. We represent multi-source data as bipartite or K-partite graphs, and investigate the spectral clustering algorithm under these representations. The algorithms are evaluated using TRECVID-2003 corpus with semantic features extracted from speech transcripts and visual concept recognition results from videos. The experiments show that the proposed bipartite clustering algorithm significantly outperforms the regular spectral clustering algorithm to capture cross-source associations.