Abstract
AbstractIn this chapter, we put forward a new technique for lecture video segmentation and key frame extraction. In this chapter, the advantages of Histogram of Oriented Gradients (HOG) features and radiometric correlation with entropic measures are explored to detect the shot boundaries and the key frames of the lecture video sequences. In the initial stage of the algorithm, HOG feature is used to project all frames into an n-dimensional feature space. The similarities between the n-dimensional extracted HOG features for two consecutive frames are obtained using radiometric correlation measure. The radiometric correlation between the successive frames of the video is found to have a significant amount of uncertainty, due to variation in color, illumination, or object motion. We have used entropic measure to find the shot boundaries. The key frames are obtained after detection of the shot boundaries by analyzing the peaks and valleys of the radiometric correlation measures. The proposed scheme is tested on several lecture video sequences and compared against six existing state-of-the-art techniques by considering two evaluation measures: computational time and shot transitions.KeywordsHistogram of Oriented GradientsRadiometric correlationEntropyShot boundaryKey frames
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