A video hard cut detection using multifractal features

Abstract

Efficient management of video sequences is based on adequate video content description. This description can be used for various purposes in different applications, telecommunication services, video and multimedia systems. Video hard cut detection represents the foundation of temporal video segmentation. In this paper, a new video hard cut detection methodology is proposed using multifractal features. Transition between two shots can be described as color and texture differences within a decoded video sequence. In the proposed methodology we formed specific structures by measuring color differences between frames. The formed structures are used for hard cut candidate detection. This is followed by multifractal representation of texture changes by Holder exponents. The proposed methodology achieves high performance using more than 750,000 frames, extracted from forty different video sequences, classified by four well known genre groups. Moreover, the proposed hard cut detection achieves high performance regardless of high level video production or complex non-linear editing for different genre groups. This is confirmed by comparison between the proposed methodology and other recent work on hard cut detection.