About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICPR 2000
Conference paper
Video summarization with supervised learning
Abstract
We present a video summarization technique based on supervised learning. Within a class of videos of similar nature, user provides the desired summaries for a subset of videos. Based on this supervised information, the summaries for other videos in the same class are generated. We derive frame-transitional features and subsequently represent each frame transition as a state. We then formulate a loss functional to quantify the discrepency between state tansitional probabilities in the original video and that in the intended summary video, and optimize this functional. We experimentally validate the performance of the technique using cross-validation scores on two different class of videos, and demonstrate that the proposed technique is able to produce high quality summarization capturing the user perception. © 2008 IEEE.