A distance measure for repeated takes of one scene

In applications, such as post-production and archiving of audiovisual material, users are confronted with large amounts of redundant unedited raw material, called rushes. Viewing and organizing this material are crucial but time consuming tasks. Typically, multiple but slightly different takes of the same scene can be found in the rushes video. We propose a method for detecting and clustering takes of one scene shot from the same or very similar camera positions. An important subproblem is to determine the similarity of video segments. We propose a distance measure based on the Longest Common Subsequence (LCSS) model. Two variants of the proposed approach, one with a threshold parameter and one with automatically determined threshold, are compared against the Dynamic Time Warping (DTW) distance measure on six videos from the TRECVID 2007 BBC rushes summarization data set. We also evaluate the influence of the applied temporal segmentation method at the input on the results. Applications of the proposed method to automatic skimming and interactive browsing of rushes video are described.