Automatic consumer video summarization by audio and visual analysis

Video summarization provides a condensed version of a video stream by analyzing the video content. Automatic summarization of consumer videos is an important tool that facilitates efficient browsing, searching, and album creation in large consumer video collections. This paper studies automatic video summarization in the consumer domain where most previous methods cannot be easily applied due to the challenging issues for content analysis, i.e., consumer videos are captured with uncontrolled conditions such as uneven illumination, clutter, and large camera motion, and with poor-quality soundtrack as a mix of multiple sound sources under severe noise. To pursue reliable summarization, a case study with actual consumer users is conducted, from which a set of consumer-oriented guidelines is obtained. The guidelines reflect the high-level semantic rules, in both visual and audio aspects, which are recognized by consumers as important to produce good video summaries. Following these guidelines, an automatic video summarization algorithm is developed where both visual and audio information are used to generate improved summaries. To the best of our knowledge, this is a first systematic study on automatic summarization of consumer-quality videos. Experimental evaluations from consumer subjects show the effectiveness of our approach.