Gradual transition detection based on bipartite graph matching approach

This paper addresses gradual transition detection which is part of video segmentation problem, and consists in identifying the boundary between consecutive shots. In this work, we propose an approach to cope with gradual transition detection in which we define and use a new dissimilarity measure based on the size of the maximum cardinality matching calculated using a bipartite graph with respect to a specified window. The experiments have used a video dataset which presents a variety of different video genres with more than 500 gradual transitions and our method with a much simpler classification approach achieves more than 90% recall with almost 80% precision which is similar to the best results found.