Broadcast Video Content Segmentation by Supervised Learning

Today’s viewers of broadcast content are presented with huge amounts of content from broadcast networks, cable networks, pay-per-view, and more. Streaming video over the internet is beginning to add to this flow. Viewers do not have enough time to watch all of this content, and in many cases, even after selecting a few programs of interest, they many want to speed up their viewing of the chosen content, either by summarizing it or by providing tools to rapidly navigate to the most important parts. New display devices and new viewing environments, for example using a cell phone to watch content while riding the bus, will also increase the need for new video summarization and management tools. Video Summarization tools can vary substantially in their goals. For example, tools may seek to create a set of still-image keyframes, or they may create a condensed video skim [14]. Even after specifying the format of the summary, there can be different semantic objectives for the summary. A summary meant to best convey the plot of a situation comedy could differ substantially from a summary meant to show the funniest few scenes from the show. Most of these processing goals remain unachieved despite over a decade of work on video summarization. The fundamental reason for this difficulty is the existence of the ”semantic gap”, the large separation between computationally easy-to-extract audio and visual features and semantically meaningful items such as spoken words, visual objects, and elements of narrative structure. Because most video summarization goals are stated in semantic terms (”the most informative summary,” ”the most exciting plays of the match”), while our computational tools are best at extracting simple features like audio energy and color histograms, we must find some way to bridge these two domains. Springer Book on Content Analysis This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2009 201 Broadway, Cambridge, Massachusetts 02139