Abstract: In this paper, a new method is proposed for video summarization and keyframe extraction using combined color, texture and motion information of the video as well as the sparse representation of local descriptors. To reduce the computational overhead of the algorithm, a non-uniform frame sampling strategy is employed using a shot detection algorithm. Subsequently, Binary Robust Invariant Scalable Keypoints (BRISK) and Histogram of Oriented Gradient (HOG) around the keypoints in the sampled frames are extracted as the local descriptors. By sparse representation and spatially partitioning of local features, the frame discriminating curve is constructed. We extract initial keyframes by detecting local maxima of frame discriminating curve and removing weak maxima. In order to remove redundant keyframes, we use similarity measure and motion model between the initial keyframes to extract final keyframes. Experimental results and the comparison of the results of the proposed algorithm with those of other methods show that the proposed algorithm enhances recall and F-measure indices.
Keywords: Keyframe extraction, shot boundary detection, sparse representation, video summarization, local descriptors
