Entropy-based fade modeling and detection
Accurate shot boundary detection techniques have been an important research topic in the last decade. Such interest is motivated by the fact that segmenting a video stream is the first step towards video content analysis and content-based video browsing and retrieval. In this paper, we present a new algorithm mainly focused on the detection of fades by using of a non-common feature in previous work: entropy, a scalar representation of the amount of information of each video frame. A survey of the properties of this feature is first provided where authors show that the pattern this series exhibits when fades occur is clear and consistent. It does not depend on the monochrome color used to fade and, besides, it is able to deal with on-screen texts remaining in the monochrome stage of them. A statistical model based algorithm to detect fades is proposed. Due to the clear pattern shown by fades in the entropy series and the accurate mathematical model used, motion and illumination changes do not severely affect precision as it normally happens with algorithms dealing with the detection of gradual transitions.
shot boundary detection video entropy series video segmentation pattern recognition correlation-based comparison