SPARSE MODELING FOR TOPIC-ORIENTED VIDEO SUMMARIZATION

While most existing video summarization approaches aim to extract an informative summary of a single video, we propose an unsupervised framework for summarizing topicrelated videos by exploring complementarity within videos. We develop a novel sparse optimization method to extract a diverse summary that is both interesting and representative in describing the video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Experimental results demonstrate that our approach clearly outperforms the state-of-the-art methods.