Clustering of video packets using interactive refinement by relevance feedback

Structuralization of the video stream is necessary for effective handling of video media. Especially, collecting similar scenes with sufficient accuracy contributes to the structuralization. In this paper, we propose a method of clustering with relevance feedback. First, fixed-length segments are clustered according to the feature of each segment. For the results of clustering, the user gives the feedback information whether each element is relevant to the cluster it belongs to. The accuracy can be improved by re-clustering based on the feedback information. Applying this method to a variety of video streams, we demonstrated the effectiveness of the relevance feedback.