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Video summarization using reinforcement learning in eigenspace

We propose video summarization using reinforcement learning. The importance score of each frame in a video is calculated from the user's actions in handling similar previous frames; if such frames were watched rather than skipped, a high score is assigned. To calculate the score, instead of using raw feature vectors extracted from images, we use feature vectors projected on eigenspace: as a result, we can deal with the features comprehensively. We also give an algorithm that uses the reinforcement learning method to create a personalized video summary. The summarization algorithm is applied to a soccer video to confirm its effectiveness.