Shot boundary detection with mutual information

We present a novel approach for shot boundary detection that uses mutual information (MI) and affine image registration. The MI measures the statistical difference between consecutive frames, while the applied affine registration compensates for camera panning and zooming. Results for different sequences are presented to illustrate the motion and zoom compensation and the robustness of MI to illumination changes. Furthermore we show that the affine registration has no effect at the shot boundaries themselves and therefore doesn’t corrupt the result. Because the presented algorithm analyses the frames sequentially, its parallelization is straightforward even for distributed memory architectures. We quantify the speed-up on a LINUX cluster and show that the communicational load of the implementation is almost negligible, resulting in an almost linear speed-up.