Video summarization using shot segmentation and local motion estimation

The video used by professionals from various fields and the general public is constantly growing, it´s important to develop efficient systems to classify and index them. The video summarization is an essential step to construct a system of indexing and searching video, it´s the process to create a short video presentation and a global vision on the important scenes of the video. This summarization consist to extract keyframes to present video content, it´s similar to extract the keywords from text document. Mainly summarization methods apply clustering algorithms to all video frames. There are computations expensive because they use a dissimilarity matrix which implies a quadratic calculation. In this context, we present a new technique for extracting video summary using an adapted shot segmentation. We apply a hierarchical clustering in each shot to extract the keyframes; the number of these keyframes is proportional to variations and movements in the shot. We propose a local motion estimation, we use a co-occurrence matrix to measure the dissimilarity between shot frames and to take account the motion in the shot.