By Alan Hanjalic
Content-Based research Of electronic Video specializes in basic matters underlying the advance of content material entry mechanisms for electronic video. It treats themes which are serious to effectively automating the video content material extraction and retrieval tactics, and comprises insurance of:- Video parsing,- Video content material indexing and representation,- Affective video content material analysis.In this good illustrated publication the writer integrates similar details presently scattered through the literature and combines it with new rules right into a unified theoretical method of video content material research. the cloth additionally indicates rules for destiny research.Systems builders, researchers and scholars operating within the sector of content-based research and retrieval of video and multimedia typically will locate this ebook important.
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Extra info for Content-based Analysis Of Digital Video
While cuts are clearly visible in the ECR time curve as sharp, highly distinguishable peaks, a series of high ECR values can be seen at places of gradual transitions. Thereby, the difference between fades on the one hand and dissolves and wipes on the other, is in the position of the local maximum of the obtained series of high ECR values. In the case a fadein/fade-out this maximum is positioned at the beginning/end of the series. At dissolves or wipes, the local maximum can be found in the middle of the ECR value series [Lie99].
An example method for matching the intensity-variance pattern CHAPTER 2 24 Figure 2-7. 3 Detector structure After we identified all issues that are to be considered in the process of shot-boundary detection, the remaining challenge is to integrate these issues into a unified detection framework. A general scheme of such a framework is illustrated in Figure 2-7. The core of the framework is the decision module where it is decided about the presence or absence of a shot boundary at a given frame k on the basis of three uncorrelated inputs: Discontinuity values z(k), Input A(k) based on discriminative information, Input B(k) based on prior information.
As the occurrence of the latter event only depends on the value of the discriminative function the two events can be considered independent. 5 we can now define the new prior probability of a boundary of the type b at the frame k as Clearly, the conditional probability can be seen as a modifier of the general prior probability of shot boundary occurrence in view of the “context” defined by the boundary class b. 31) can be referred to as context-dependent prior probability of shot boundary occurrence.