A NEW DISSIMILARITY MEASURE FOR CUT DETECTION USING BIPARTITE GRAPH MATCHING

Abstract

Cut detection is part of the video segmentation problem, and consists in identifying the boundary between two consecutive shots. In this case, when two consecutive frames are similar, they are considered to be in the same shot. This work presents an approach to cut detection using a new simple and efficient dissimilarity measure (which is also invariant to rotation and translation) based on the size of a bipartite graph matching. To establish some parameter values, a machine learning approach is used. Experimental results provides a comparison between the new approach and other popular algorithms from the literature, showing that the new algorithm is robust and has a high performance compared to other methods for cut detection.