Graphics Multimedia

Content-based Analysis Of Digital Video by Alan Hanjalic

By Alan Hanjalic

Content-Based research Of electronic Video makes a speciality of basic matters underlying the improvement of content material entry mechanisms for electronic video. It treats subject matters which are severe to effectively automating the video content material extraction and retrieval strategies, and contains insurance of:- Video parsing,- Video content material indexing and representation,- Affective video content material analysis.In this good illustrated ebook the writer integrates similar info presently scattered through the literature and combines it with new rules right into a unified theoretical method of video content material research. the fabric 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 in most cases will locate this e-book beneficial.

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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].

Interestingly, all color spaces performed better than the luminance alone (grey-level histograms), which indicates a high importance of the color content of the video frame in detecting shot boundaries. While performing best, the Munsell color space is, however, also computationally most intensive. The L*a*b* color space DETECTING SHOT BOUNDARIES IN VIDEO 29 appears to be the best choice if the optimum between the detection performance and computational cost is searched for. Histograms can also be computed and compared block-wise.

5), is the j-th bin of the histogram of the C - component of the color space used. Another popular metric for histogram comparison is the so-called [Nag92] that can generally be formulated as 28 CHAPTER 2 Zhang et al. 6) does not only enhance the discontinuities across a shot boundary but also the effects caused by camera/object motion. 4), whereas it does require more computational power. We mention here also the histogram intersection as a further example of a frequently used metric for histogram similarity computation.

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