Video summarization by k-medoid clustering

In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k-medoid clustering algorithms to find the best representative frame for each video shot. This algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity clustering according to the given index. In our proposal, the distance between frames is calculated using a fast full search block matching algorithm based on the frequency domain. The proposed approach is computationally tractable and robust with respect to sudden changes in mean intensity within a shot. Additionally, this approach produces different key frames even in the presence of large motion. The experiments results show that our algorithm extracts multiple representatives frames in each video shot without visual redundancy, and thus it is an effective tool for video indexing and retrieval.