Video summarization using reinforcement learning in eigenspace
K. Masumitsu, Tomio Echigo · 2000
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.