An unified framework for shot boundary detection via active learning

Abstract

Video shot boundary detection is an important step in many video processing applications. We observe that video shot boundary is a multi-resolution edge phenomenon in the feature space. In this paper, we expanded our previous temporal multi-resolution analysis (TMRA) work by introducing the new feature vector based on motion. Further we employ the support vector machine (SVM) to refine the classification of shot boundaries. The resulting framework has been tested on the MPEG 7 video data set, and has been shown to have good accuracy for both the detection of abrupt and gradual transitions as well as their boundaries. It also has good noise tolerance characteristics.