Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines

Video shot detection is an important contemporary problem since it is the first step towards indexing and content based video retrieval. Traditionally, video shot segmentation approaches rely on thresholding methodologies which are sensitive to the content of the video being processed and do not generalize well the when there is little prior knowledge about the video content. To ameliorate this shortcoming we propose a learning based methodology using a set of features that are specifically designed to capture the differences among hard cuts, gradual transitions and normal sequences of frames at the same time. A support vector machine (SVM) classifier is trained both to locate shot boundaries and characterize transition types. Numerical experiments using a variety of videos demonstrate that our method is capable of accurately discriminating shot transitions in videos with different characteristics.