Shot detection combining Bayesian and structural information

There are a number of shots in a video, each of which has boundary types, such as cut, fade, dissolve and wipe. Many previous approaches can find the cut boundary without difficulty. Ho wever, most of them often produce false alarms for the videos with large motions of camera and objects. We propose a shot boundary detection method combining Bayesian and structural information. In the Bayesian approach, a probability distribution function models each transition type, e.g., normal, abrupt, gradual transition, and also models shot length. But inseparability between those distributions causes unwanted results and degrades the precision. In this paper, we demonstrate that the shape of the filtered frame difference, called the structural information, provides an important cue to distinguish fade and dissolve effects from cut effects and gradual changes caused by motion of camera and objects. The proposed method has been tested for a few golf video segments and shown good performances in detecting fade and dissolve effects as well as cut.