NDLI: A novel vector quantization-based video summarization method using independent component analysis mixture model

Content Provider	IEEE Xplore Digital Library
Author	Junfeng Jiang Xiao-Ping Zhang
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada, M5B 2K3 (Junfeng Jiang; Xiao-Ping Zhang)
Abstract	In this paper, we present a new independent component analysis mixture vector quantization (ICAMVQ) method to summarize the video content. In particular, independent component analysis (ICA) is applied first to explore the characteristics of video data and build a compact 2D feature space. A new ICAMVQ method is then developed to find the optimized quantization codebook in ICA subspace. The optimal codebook size is determined by Bayes information criterion (BIC). The frames that are the nearest neighbors to the quanta in the ICAMVQ codebook are sampled to summarize the video. A 2D kD-tree is employed to index the feature space and accelerate the nearest-neighbor search. Experimental results show that our method is practically effective and computationally efficient to build a video summarization system.
Starting Page	1341
Ending Page	1344
File Size	381278
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5946660
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization Hidden Markov models Independent component analysis Color Nearest neighbor searches Streaming media Histograms vector quantization Video summarization independent component analysis mixture model
Content Type	Text
Resource Type	Article

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