Shot Boundary Detection Based on SVMs via Visual Attention Features

Shot boundary detection (SBD) is the basis of interpreting video content, event, and relevant knowledge. As existing SBD algorithms are sensitive to video object motion and no reliable solution exists to provide accurate shot boundary detection, it still remains an unsolved problem. We propose a new algorithm of shot boundary detection in this paper, which employs support vector machine (SVM) as a classifier to detect shot boundary. The proposed SBD algorithm introduces the concept of the visual attention features based on the research results of psychology, which presents advantages in its robustness to video object motion. Extensive experimental results carried out on the TRECVID 2007 database show that the proposed algorithm works well in detecting shot boundary measured by both recall and precision.