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GREEDY SALIENT DICTIONARY LEARNING WITH OPTIMAL POINT RECONSTRUCTION FOR ACTIVITY VIDEO SUMMARIZATION

Salient dictionary learning has recently proven to be effective for unsupervised activity video summarization by key-frame extraction. All relevant methods select a small subset of the original data points/video frames as dictionary atoms/representatives that, in concert, both optimally reconstruct the original entire dataset/video sequence and are salient. Therefore, they attempt to simultaneously optimize a reconstruction term, pushing towards a dictionary/summary that best reconstructs the entire dataset, and a saliency term, pushing towards a dictionary composed of salient data points. In this paper, a hypothesis is proposed and empirically tested, namely that more salient data points can be obtained by attempting to restrain reconstruction error separately for each original data point. Thus, salient dictionary learning is extended by adding a third term to the objective function, pushing towards optimal point reconstruction. A pre-existing greedy, iterative algorithm for salient dictionary learning is modified according to the proposed extension in two alternative ways. The resulting methods achieve state-of-the-art performance in three databases, verifying the validity of our hypothesis.