TY - GEN
T1 - Video semantic concept detection via associative classification
AU - Lin, Lin
AU - Shyu, Mei Ling
AU - Ravitz, Guy
AU - Chen, Shu Ching
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with a new associative classification algorithm which generates the classification rules based on the correlation between different feature-value pairs and the concept classes by using Multiple Correspondence Analysis (MCA). Experimenting with the high-level features and benchmark data sets from TRECVID, our proposed algorithm achieves promising performance and outperforms three well-known classifiers which are commonly used for performance comparison in the TRECVID community.
AB - Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with a new associative classification algorithm which generates the classification rules based on the correlation between different feature-value pairs and the concept classes by using Multiple Correspondence Analysis (MCA). Experimenting with the high-level features and benchmark data sets from TRECVID, our proposed algorithm achieves promising performance and outperforms three well-known classifiers which are commonly used for performance comparison in the TRECVID community.
KW - Associative classification
KW - Concept detection
KW - Multiple correspondence analysis
UR - http://www.scopus.com/inward/record.url?scp=70449585631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449585631&partnerID=8YFLogxK
U2 - 10.1109/ICME.2009.5202523
DO - 10.1109/ICME.2009.5202523
M3 - Conference contribution
AN - SCOPUS:70449585631
SN - 9781424442911
T3 - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
SP - 418
EP - 421
BT - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
T2 - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Y2 - 28 June 2009 through 3 July 2009
ER -