Entropy-Based Video Retrieval

With the development of video compress technology and network translatetechnology, the video information is already become to the mainly method of peopleobtain information because of video have the advantage of intuitionistic and moreinformation. How to organizing and managing those video data to convenient forpeople to find the needed information expediently and fleetly is a worthy question toresearch. Video retrieval aim is to find the needed video slip from a mass of videodata. Key word is write by manual in traditional key word-based retrieve method, it isinefficient and it is different to offer the client the needed information due to itssubjectivity. In order to automatic fleetly accurately find the needed video clips froma mass of video, it need implement the content-based video retrieve.Shot is the essence unit of video. Content-based video retrieve produce includethree step: the first is shot boundary detection, namely, it is translate boundarydetection between different shots; secondly, key-frame extraction, namely, it is toextract one frame or several frame from each shot, those key-frames can used tovideo content's quickly browse and index of video retrieval; thirdly, similarly contentshots to cluster to scene.Shot detection is the importantly first step of video analysis, it direct influencethe effective of video retrieval. Recently, people already do many research to shotdetection. But it still have many issue not to solve due to video peculiarity, such ashow to confirm the accurate translate frame, how to detect the amphibolous gradualshot translation, how to select suitable automatic threshold, those still have auniversal effectively method. So that the paper take the shot detection to researchemphases.This paper research emphases is that apply the information entropy's knowledgeto video shot detection. In the fourth chapter, we discuss the continuous inter-framesentropy difference apply to shot detection, combine gauss model and slide windowmethod to decide the automatic threshold, and use the subsection best threshold decided by maximum entropy segmenting to eliminate the fault detection owing tothe local maximum of slide window. Using interval inter-frames combine theautomatic threshold decided at prior.In the fifth chapter, we discuss the mutual information apply to shot detection,because of the difference is larger in the scale of the same shot, so that it is notimmediately using routinely automatic threshold decision method, this paper proposethe abrupt shot detection decision method combine mean and gauss model. Usinginterval inter-frames mutual information to detection the shot gradual change.After shots are generated from video clips by shot boundary detection algorithm,we need to extract the key frames from the shots to achieve the effectively indexing.In the sixth chapter, we discuss the entropy difference and mutual information applyto video key frame extraction.