Selecting Video Key Frames Based on Relative Entropy and the Extreme Studentized Deviate Test

This paper studies the relative entropy and its square root as distance measures of neighboring video frames for video key frame extraction. We develop a novel approach handling both common and wavelet video sequences, in which the extreme Studentized deviate test is exploited to identify shot boundaries for segmenting a video sequence into shots. Then, video shots can be divided into different sub-shots, according to whether the video content change is large or not, and key frames are extracted from sub-shots. The proposed technique is general, effective and efficient to deal with video sequences of any kind. Our new approach can offer optional additional multiscale summarizations of video data, achieving a balance between having more details and maintaining less redundancy. Extensive experimental results show that the new scheme obtains very encouraging results in video key frame extraction, in terms of both objective evaluation metrics and subjective visual perception ​
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