Detection of gradual transitions on intensity-time distributions using partial linear indicator

Abstract

Video partitioning, which segments a video sequence into a meaningful group, is a basic technique for automatic video indexing and video editing. In this paper, we present a statistical analysis of video transitions in intensity-time distributions and suggest an implementation using partial linear indicator for fast video partitioning. Since it is very difficult to detect the gradual transitions of intensity variations, such as dissolve, fade and wipe, out of camera panning and object moving using a single measure, we analyze intensity variations through time frames and formalize the editing effects to the intensity distributions using variations of means and variances. Based on the idea that the intensity change introduced by editings, as opposed to motions, is more uniform, a fast method to extract the patterns of gradual transitions within motions is described. This method detects the gradually changed editing patterns and classifies the patterns into analyzed editing classes, even if there are simultaneous motions. The experimental results of our method present the effectiveness in detecting gradual transitions and the efficiency in processing time.