A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
A robust scene-change detection method for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we introduce a simple method for the detection of hard cuts using only interframe differences. The method is inspired in the computational gestalt theory. The key idea in this theory is to define a meaningful event as large deviation from the expected background process. That is, an event that has little probability to occur given a probabilistic background model. In our case we will define a hard cut when the interframe differences have little probability to be produced by a given model of interframe differences of non-cut frames. Since we only use interframe differences, there is no need to perform motion estimation, or other type of processing, and the method turns to be very simple with low computational cost. The proposed method outperforms similar methods proposed in the literature.