NDLI: Representative and diverse video summarization

Content Provider	IEEE Xplore Digital Library
Author	Xiao Chen Xuelong Li Xiaoqiang Lu
Copyright Year	2015
Description	Author affiliation: Center for Opt. IMagery Anal. & Learning (OPTIMAL), Xi'an Inst. of Opt. & Precision Mech., Xi'an, China (Xiao Chen; Xuelong Li; Xiaoqiang Lu)
Abstract	Video summarization usually refers to produce a summary preserving essential content of the original video. Many existing methods have been developed to select representative frames by a dictionary learning model, which have led to a state-of-the-art performance. However, learning dictionary without considering relationship between samples of the original data space would lead to imprecise representation. To address this problem, in this paper, geometrical distribution information of samples is incorporated into the dictionary learning process. A graph based learning strategy is employed to draw the geometrical distribution information. Meanwhile, the diversity criteria is considered as important as representativeness, which can reduce redundant frames to be selected in final summary. Thus similarity measuring is imported to guarantee that a final summary contains diversity contents within the original video. The proposed method is validated on a challenging and widely used dataset, and state-of-the-art performance is achieved in contrast to other methods.
Starting Page	142
Ending Page	146
File Size	115619
Page Count	5
File Format	PDF
e-ISBN	9781479919482
DOI	10.1109/ChinaSIP.2015.7230379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Visualization Structural information Dictionaries Color Consumer video Feature extraction Cameras Sparse representation Video summarization Image reconstruction Dictionary learning
Content Type	Text
Resource Type	Article

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