Bayesian Shot Detection Using Structural Weighting

A video stream consists of a number of shots each of which has different boundary types such as cut, fade, and dissolve. 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. In this paper, we demonstrate that the shape of the histogram difference between two successive color images, called the structural information, provides an important cue to distinguish fade and dissolve effects from cut effect. Our shot detection method uses an optimal Bayesian classifier weighted by the structural information to model the gradual transitions such as fades and dissolves. 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.

• Type: Conference
• Year: 2000
• Where: MVA
• Authors: Seung-Hoon Han, In-So Kweon, Chang-Yeong Kim, Yang Seck Seo
• Keywords: Cut Boundary, MVA 2000, MVA 2007, Optimal Bayesian Classifier, Structural Information