In this paper, we present an approach for detecting slide transitions in lectures videos by introducing sparse time-varying graphs. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find the keyframes where slide content changes. Specifically, we first partition the lecture video into short segments through feature detection and matching. By constructing a sparse graph at each moment with short video segments as nodes, we formulate the detection problem as a graph inference issue. A set of adjacency matrix between edges, which are sparse and time-varying, are then solved through a global optimization algorithm. Consequently, the changes between adjacency matrix reflect the slide transition. Experimental results show that the proposed system achieves the better accuracy than other video summarization and slide progression detection approaches.
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