TY - GEN
T1 - Large scale incremental web video categorization
AU - Zhang, Xu
AU - Song, Yi Cheng
AU - Cao, Juan
AU - Zhang, Yong Dong
AU - Li, Jin Tao
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Concept collection
KW - Incremental learning
KW - Large scale
KW - Similarity measurement
KW - Web video categorization
KW - n-ISVM
UR - http://www.scopus.com/inward/record.url?scp=72249117762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72249117762&partnerID=8YFLogxK
U2 - 10.1145/1631135.1631142
DO - 10.1145/1631135.1631142
M3 - Conference contribution
AN - SCOPUS:72249117762
SN - 9781605587615
T3 - 1st International Workshop on Web-Scale Multimedia Corpus, WSMC'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09
SP - 33
EP - 40
BT - 1st International Workshop on Web-Scale Multimedia Corpus, WSMC'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09
T2 - 1st International Workshop on Web-Scale Multimedia Corpus, WSMC'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09
Y2 - 19 October 2009 through 24 October 2009
ER -