Video Keyframe Analysis Using a Segment-Based Statistical Metric in a Visually Sensitive Parametric Space

@article{Omidyeganeh2011VideoKA,
  title={Video Keyframe Analysis Using a Segment-Based Statistical Metric in a Visually Sensitive Parametric Space},
  author={Mona Omidyeganeh and Shahrokh Ghaemmaghami and Shervin Shirmohammadi},
  journal={IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
  year={2011},
  volume={20 10},
  pages={2730-7}
}
This paper addresses a new approach to the keyframe extraction problem employing generalized Gaussian density (GGD) parameters of wavelet transform subbands along with Kullback-Leibler distance (KLD) measurement. Shot and cluster boundaries are selected using KLDs between GGD feature vectors, and then keyframes are located based on similarity and dissimilarity criteria. Objective and subjective evaluations show the high accuracy of this new approach compared with traditional methods. 
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