Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97228
Title: Towards scalable summarization of consumer videos via sparse dictionary selection
Authors: Cong, Yang
Yuan, Junsong
Luo, Jiebo
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: Cong, Y., Yuan, J., & Luo, J. (2012). Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection. IEEE Transactions on Multimedia, 14(1), 66-75.
Series/Report no.: IEEE transactions on multimedia
Abstract: The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm.
URI: https://hdl.handle.net/10356/97228
http://hdl.handle.net/10220/11469
ISSN: 1520-9210
DOI: 10.1109/TMM.2011.2166951
Rights: © 2011 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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