We present a technique for genre-independent scene-change detection using audio and video features in a discriminative
support vector machine (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 improves 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.