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Large scale incremental web video categorization
Abstract With the advent of video sharing websites,With the advent of video sharing websites, the amount of videos on the internet grows rapidly. Web video categorization is an efficient methodology for organizing the huge amount of videos. In this paper we investigate the characteristics of web videos, and make two contributions for the large scale incremental web video categorization. First, we develop an effective semantic feature space Concept Collection for Web Video with Categorization Distinguishability (CCWV-CD), which is consisted of concepts with small semantic gap, and the concept correlations are diffused by a novel Wikipedia Propagation (WP) method. Second, we propose an incremental support vector machine with fixed number of support vectors (n-ISVM) for large scale incremental learning. To evaluate the performance of CCWV-CD, WP and n-ISVM, we conduct extensive experiments on the dataset of 80,021 most representative videos on a video sharing website. The experiment results show that the CCWV-CD and WP is more representative for web videos, and the n-ISVM algorithm greatly improves the efficiency in the situation of incremental learning. Copyright 2009 ACM. incremental learning. Copyright 2009 ACM.
Abstractsub With the advent of video sharing websites,With the advent of video sharing websites, the amount of videos on the internet grows rapidly. Web video categorization is an efficient methodology for organizing the huge amount of videos. In this paper we investigate the characteristics of web videos, and make two contributions for the large scale incremental web video categorization. First, we develop an effective semantic feature space Concept Collection for Web Video with Categorization Distinguishability (CCWV-CD), which is consisted of concepts with small semantic gap, and the concept correlations are diffused by a novel Wikipedia Propagation (WP) method. Second, we propose an incremental support vector machine with fixed number of support vectors (n-ISVM) for large scale incremental learning. To evaluate the performance of CCWV-CD, WP and n-ISVM, we conduct extensive experiments on the dataset of 80,021 most representative videos on a video sharing website. The experiment results show that the CCWV-CD and WP is more representative for web videos, and the n-ISVM algorithm greatly improves the efficiency in the situation of incremental learning. Copyright 2009 ACM. incremental learning. Copyright 2009 ACM.
Bibtextype inproceedings  +
Doi 10.1145/1631135.1631142  +
Has author Xiaodan Zhang + , Song Y.-C. + , Cao J. + , Zhang Y.-D. + , Li J.-T. +
Has extra keyword Concept correlation + , Dataset + , Distinguishability + , Fixed numbers + , Incremental learning + , Incremental support vector machine + , Semantic features + , Semantic gap + , Similarity measurements + , Support vector + , Video sharing + , Web video + , Wikipedia + , Data storage equipment + , Education + , Learning algorithms + , Method of moments + , Semantics + , Support vector machines + , World Wide Web + , Multimedia systems +
Has keyword Concept collection + , Incremental learning + , Large scale + , N-ISVM + , Similarity measurement + , Web video categorization +
Isbn 9781605587615  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 33–40  +
Published in 1st International Workshop on Web-Scale Multimedia Corpus, WSMC'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09 +
Title Large scale incremental web video categorization +
Type conference paper  +
Year 2009 +
Creation dateThis property is a special property in this wiki. 8 November 2014 03:16:00  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 03:16:00  +
DateThis property is a special property in this wiki. 2009  +
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