Automatic shot change detection algorithm using multi-stage clustering for MPEG-compressed videos

Automatic shot change detection has been recognized as an important research issue for video classification. This paper proposes an automatic clustering-based algorithm for shot change detection in MPEG-compressed videos with a small number of user-defined parameters. For accurate detection of abrupt and gradual shot changes, the proper selection and extraction of features are important. We first propose a fast edge image extraction scheme in the DCT domain on the basis of AC prediction. Then, by using the features extracted from the edge images and DC images, a two-stage clustering-based algorithm is proposed for shot change detection. In the first stage, the algorithm detects abrupt shot changes by employing two-means clustering on the 2-D feature space of histogram and pixel differences between two neighboring DC frames. In the next stage, it subsequently explores gradual shot changes between two adjacent abrupt shot changes by performing a two-step clustering scheme, which uses multiple features such as an edge energy diagram and several frame difference measures. Simulation results show that the proposed algorithm is fast and accurate.