Sequence Kernels for Clustering and Visualizing Near Duplicate Video Segments

Organizing and visualizing video collections containing a high number of near duplicates is an important problem in film and video post-production. While kernels for matching sequences of feature vectors have been used e.g. for classification of video segments, kernel-based methods have not yet been applied to matching near duplicate video segments. In this paper we survey the application of six sequence-based kernels to clustering near duplicate video segments using kernel k-means
 and hierarchical clustering, and the application of kernel PCA for generating visualizations of content sets suitable for browsing. Evaluation on the TRECVID 2007 BBC rushes data set shows that the results of the kernel based methods are comparable to other approaches for matching near duplicates, eliminating differences between dynamic time warping and string matching based approaches. These results show that hierarchical clustering on the kernel matrix outperforms kernel k-means. We also show that well-arranged visualizations of both single- and multi-view content sets can be obtained using kernel PCA.