Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009 |
| Pages | 418-421 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Nov 20 2009 |
| Event | 2009 IEEE International Conference on Multimedia and Expo, ICME 2009 - New York, NY, United States Duration: Jun 28 2009 → Jul 3 2009 |
Other
| Other | 2009 IEEE International Conference on Multimedia and Expo, ICME 2009 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 6/28/09 → 7/3/09 |
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Keywords
- Associative classification
- Concept detection
- Multiple correspondence analysis
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Networks and Communications
- Hardware and Architecture
- Software
Cite this
Video semantic concept detection via associative classification. / Lin, Lin; Shyu, Mei-Ling; Ravitz, Guy; Chen, Shu Ching.
Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009. 2009. p. 418-421 5202523.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Video semantic concept detection via associative classification
AU - Lin, Lin
AU - Shyu, Mei-Ling
AU - Ravitz, Guy
AU - Chen, Shu Ching
PY - 2009/11/20
Y1 - 2009/11/20
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
SN - 9781424442911
SP - 418
EP - 421
BT - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
ER -