Efficient matching and clustering of video shots

Browsing, search and retrieval in digital video libraries depend on the ability of the system to match, classify and group video shots by their visual contents. However, similarity of shots cannot always be settled by using only one key frame per shot as commonly practiced. In this paper we propose a scheme to match video shots and to cluster them by taking into account the temporal variations within individual shots. A much reduced representation for a video shot is used. These images still capture the dynamics of visual contents for matching and clustering process. Experimental results are reported