Scene pathfinder: unsupervised clustering techniques for movie scenes extraction

The need for watching movies is in perpetual increase due to the widespread of the internet and the increasing popularity of the video on demand service. The important mass of movies stored in the Internet or in VOD servers need to be structured to accelerate the browsing operation. In this paper, we propose a new system called "The Scene Pathfinder" that aims at segmenting the movies into scenes to give users the opportunity to have a non- sequential access and to watch particular scenes of the movie. This helps them to judge quickly the movie and decide if they have to buy or to download it and avoiding waste of time and money. The proposed approach is multimodal. We use both of visual and auditory information to accomplish the segmentation. We base on the assumption that every movie scene is either action or non- action scene. Non-action scenes are generally characterized by static backgrounds and occur in the same place. For this reason, we base on the content information and on the Kohonen map to extract these kinds of scenes (shots agglomerations). Action scenes are characterized by high tempo and motion. For this reason, we base on tempo features and on the Fuzzy CMeans to classify shots and to localize the action zones. The two processes are complementary. Indeed, the over segmentation that may occur in the extraction of action scenes by basing on the content information is repaired by the Fuzzy clustering. Our system is tested on a varied database and obtained results show the merit of our approach and that our assumptions are well-founded.