Video slide shows constitute a very frequent video type. Typically, in order to produce such a video, a series of still images are used and processed. We present a method to solve the video slide show inversion problem where we are given a video slide show and we wish to extract the sequence of still images that have been used to produce this video. Our approach relies on a fast and efficient key-frame extraction method that partitions the video into content homogeneous segments and extracts a representative key-frame for each video segment. An important characteristic of this method is that the number of key-frames is determined automatically. Next, the set of key-frames is further processed to solve various problems such as mixing of images, fade-in/fade-out and zooming effects that produce keyframes not belonging to the original image sequence, thus they must be detected and discarded. We provide illustrative examples from the application of the method on several video slide shows with different characteristics.
An efficient shot summarization method is presented based on agglomerative clustering of the shot frames. Unlike other agglomerative methods, our approach relies on a cluster merging criterion that computes the content homogeneity of a merged cluster. An important feature of the proposed approach is the automatic estimation of the number of a shot's most representative frames, called keyframes. The method starts by splitting each video sequence into small, equal sized clusters (segments). Then, agglomerative clustering is performed, where from the current set of clusters, a pair of clusters is selected and merged to form a larger unimodal (homogeneous) cluster. The algorithm proceeds until no further cluster merging is possible. At the end, the medoid of each of the final clusters is selected as keyframe and the set of keyframes constitutes the summary of the shot. Numerical experiments demonstrate that our method reasonable estimates the number of ground-truth keyframes, while extracting non-repetitive keyframes that efficiently summarize the content of each shot.
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