Dissolve detection in videos using an ensemble approach

Detection of shot transitions is an important preprocessing step for content based video retrieval systems. Out of the various types of transitions present in videos, dissolve detection is the most challenging one. This is due to the inherent complexity induced by the component frames making up the dissolve transition. In this work, a two-phased approach is presented for detecting the dissolve sequences. The first phase is concerned with identifying candidate dissolve sequences based on the parabolic nature of the mean fuzzy entropy data computed on the composing frames of the video. In the second phase, the candidates are filtered through multiple stages where each stage is based on a low-level feature of the video stream. The threshold in each stage is based on the data obtained for that feature from the constituent frames of the video. The final set of dissolve sequences are obtained at the end of the filtration stage. The proposed method is also able to detect the span of the dissolve sequence with an error of maximum one frame. Comparisons reveal that the proposed method outperforms the state-of-the-art methods in terms of both recall and precision.