title: Video keyframe analysis using a segment-based statistical metric in a visually sensitive parametric space.
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.
authors.affiliation: Department of Electrical Engineering, Sharif University of Technology and the Distributed and Collaborative Virtual Environment Research Lab., University of Ottawa, Ottawa, ON K1N6N5, Canada. m_omid@ee.sharif.edu
authors.forename: Mona
authors.initials: M
authors.lastname: Omidyeganeh
authors.forename: Shahrokh
authors.initials: S
authors.lastname: Ghaemmaghami
authors.forename: Shervin
authors.initials: S
authors.lastname: Shirmohammadi