A Comparison of Distance Measures for Clustering Video Sequences

Matching video segments in order to detect their similarity is a necessary task in retrieval and summarization applications. In order to determine nearly identical content, such as repeated takes of the same scene, very precise matching of sequences of features extracted from the video segments needs to be performed. In this paper we compare the performance of three distance measures for the task of clustering multiple takes of the same scene: dynamic time warping (DTW) and two variants of longest common subsequence (LCSS). We also evaluate the influence of the quality of the input segmentation on the performance of the algorithms.