New application of graph mining to video analysis

Given a graph, frequent graph mining extracts subgraphs appearing frequently as useful knowledge. This paper proposes to exploit graph mining that discovers knowledge without supervision to realize unsupervised image analysis. In particular, we present a background subtraction algorithm from videos in which the background model is acquired without supervision. The targets of our algorithm are videos in which a moving object passes in front of a surveillance camera. After transforming each video frame into a region adjacency graph, our method discovers the subgraph representing the background, exploiting the fact that the background appears in more frames than the moving object.