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A probabilistic framework of selecting effective key frames for video browsing and indexing

To represent effectively the video content, for browsing, indexing and video skimming, the most characteristic frames (called key-frames) should be extracted from given shots. This paper, briefly reviews and evaluates the existing approaches of key-frames extraction; and then introduces a framework of selecting effective key-frames using an unsupervised clustering method. The mixture of Gaussians is used to model the temporal variation of the feature vectors of all frames in the shot. As a result, the feature-based representation of the shot is partitioned into several clusters. From each obtained cluster, firstly the closest frame to the median of its frames is selected as a reference key-frame. Then depending on the variation in time and appearance of the cluster content against the reference key-frame multiple frames can be extracted to represent effectively the cluster. The number of clusters is determined automatically by the Bayes Information Criterion. Experimental results on tracked objects in a real-world video stream are presented which illustrate the performance of the proposed technique.