Video-shot transition detection using spatio-temporal analysis and fuzzy classification

In this paper, we propose a feature-based scheme for detecting different genres of video shot transitions based on spatio-temporal analysis and model parameter estimation. We use the histogram difference and a modified version of it to detect cuts and reduce the impact of fleeting lights. To deal with dissolves, a hybrid algorithm composed of adaptive thresholding, parameter calculation, and transition duration refinement is proposed. In addition, to find a precise duration, we develop a transition duration refinement method to examine each dissolve candidate. The associated model parameters of the dissolve are also estimated as features. After feature extraction, a fuzzy classifier integrates the features to distinguish non-transitions, cuts, and dissolves from one to another. The results of experiments demonstrate that the proposed scheme not only detects cuts and dissolves well, but also accurately locates the duration of each dissolve transition. In addition, the proposed scheme outperforms existing methods in terms of cut and dissolve detection.