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. (C) 2001 Academic Press.