Key frames extraction using graph modularity clustering for efficient video summarization

Keyframe extraction is one of the basic procedures relating to video retrieval and summary. It consists on presenting an abstract of the video with the most representative frames. This paper presents an efficient keyframe extraction approach based on local description and graph modularity clustering. The first step is to generate a set of candidate keyframes using a windowing rule in order to reduce the data to be examined. After that, detect interest points in these set of images. Then compute repeatability between each two images belonging to the candidate set and stocks these values in a matrix that we called repeatability matrix. Finally, the repeatability matrix is modelled by an oriented graph and we will select keyframes using graph modularity clustering principle. The experiments showed that this method succeeds in extracting keyframes while preserving the salient content of the video. Further, we found good values in term of precision, PSNR and compression rate.