Discriminative Genre-Independent Audio-Visual Scene Change Detection

We present a technique for genre-independent scene-change detection using audio and video features in a discriminative support vector machines (SVM) framework. This work builds on our previous work by adding a video feature based on the MPEG-7 ”scalable color” descriptor. Adding this feature imporoves our detection rate over all genres by 5% to 15% for a fixed false positive rate of 10%. We also find that the genres that benefit the most are those with which the previous audio-only was least effective. SPIE Electronic Imaging 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