Efficient Video Summarization Based on Motion SIFT-Distribution Histogram

Abstract

Video summarization refers to the process of recapitulating video stream by producing an abstract of the salient keyframes that could cover its overall content. However, an efficient video summarization requires an efficient video Shot Boundary Detection (SBD) and keyframes extraction. In this backdrop, this paper presents a novel and efficient approach for video SBD and keyframes extraction that will lead in the summarizing video. Meanly, Motion SIFT-Distribution Histogram (MoSIFT-DH) is extracted from the frames as a Glocal feature. The shot boundaries are detected using an adaptive threshold for the computed distance of MoSIFT-DH of the consecutive frames. Furthermore, keyframe representing the salient content of each segmented shot is extracted using entropy based singular values. Finally, the summarizing video is generated, by combining all the extracted keyframes. Our experiments on various videos indicate that our method can efficiently detect shot boundaries under different levels of illumination, camera operations and motion effects.