Fast shot segmentation combining global and local visual descriptors

This paper introduces an algorithm for fast temporal segmentation of videos into shots. The proposed method detects abrupt and gradual transitions, based on the visual similarity of neighboring frames of the video. The descriptive efficiency of both local (SURF) and global (HSV histograms) descriptors is exploited for assessing frame similarity, while GPU-based processing is used for accelerating the analysis. Specifically, abrupt transitions are initially detected between successive video frames where there is a sharp change in the visual content, which is expressed by a very low similarity score. Then, the calculated scores are further analysed for the identification of frame-sequences where a progressive change of the visual content takes place and, in this way gradual transitions are detected. Finally, a post-processing step is performed aiming to identify outliers due to object/camera movement and flash-lights. The experiments show that the proposed algorithm achieves high accuracy while being capable of faster-than-real-time analysis.