A STATISTICAL APPROACH TO ROBUST VIDEO TEMPORAL INDEXING AND SEGMENTATION

Abstract

For video indexing, the problem of video cut detection remains largely an open problem because of the wide nature of transitions that occur in a digital video. This paper describes a shot boundary detection technique, which is an amalgamation of few statistical methods and measures, and robustly detects camera breaks in a full-motion video clip. The proposed algorithm incorporates a weighted histogram, an error-propagation technique for increased robustness, and a curve-fitting technique to extract partitions from the similarity curve for avoiding heuristically chosen threshold value. The algorithm has been validated on many video clips and is shown to give improved results, including for videos with rapid scanning, changes in illumination, fade-ins and fade-outs, and with special effects like dissolve and filters.