Video shot boundary detection based on multi-level features collaboration

Video shot boundary detection (SBD) is a basic work of content-based video retrieval and analysis. Various SBD methods have been proposed; however, there exist limitations in the complexity of boundary detection process. In this paper, a simple yet efficient SBD method is proposed, and the aim here is to speed up the boundary detection and simplify the detection process without loss of detection recall and accuracy. In our proposed model, we mainly use a top-down zoom rule, the image color feature, and local descriptors and combine a kind of motion area extraction algorithm to achieve shot boundary detection. Firstly, we select candidate transition segments via color histogram and the speeded-up robust features. Then, we perform cut transition detection through uneven slice matching, pixel difference, and color histogram. Finally, we perform gradual transition detection by the motion area extraction, scale-invariant feature transform, and even slice matching. The experiment is evaluated on the TRECVid2001 and the TRECVid2007 video datasets, and the experimental results show that our proposed method improves the recall, accuracy, and the detection speed, compared with some other related SBD methods.