Title :
Video Keyframe Analysis Using a Segment-Based Statistical Metric in a Visually Sensitive Parametric Space
Author :
Omidyeganeh, Mona ; Ghaemmaghami, Shahrokh ; Shirmohammadi, Shervin
Author_Institution :
Dept. of Electr. Eng., Univ. of Ottawa, Ottawa, ON, Canada
Abstract :
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.
Keywords :
Gaussian processes; distance measurement; feature extraction; image segmentation; video signal processing; wavelet transforms; Kullback-Leibler distance measurement; generalized Gaussian density parameters; keyframe extraction problem; segment-based statistical metric; video keyframe analysis; visually sensitive parametric space; wavelet transform subbands; Accuracy; Feature extraction; Humans; Video sequences; Wavelet domain; Wavelet transforms; Generalized Gaussian density (GGD); Kullback–Leibler distance (KLD); video keyframe extraction;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2011.2143421
