Abstract
Association rule mining (ARM) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high efficiency and accuracy. Two important processes in mining the association rules for classification are rule generation and rule selection. In this paper, a novel high-level feature detection framework using the ARM technique together with the correlations among the feature-value pairs is proposed. A new association rule mining (ARM) algorithm has been developed, where the N-feature-value pairs are generated using a combined measure based on (1) the existence of the (N-1)-feature-value pairs (where N is larger than 1), (2) the correlation between different N-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intrasimilarity. The final association classification rules are selected by using the calculated harmonic mean of the similarity values. The proposed framework enables the automatic discovery and generation of the N-feature-value pair association rules from the 1-feature-value pairs for classification. Experimenting with 15 high-level features (concepts) and benchmark data sets from TRECVID, our proposed framework achieves promising performance and outperforms three other well-known classifiers (Decision Trees, Support Vector Machine, and Neural Networks) which are commonly used for performance comparison in the TRECVID community.
| Original language | English |
|---|---|
| Title of host publication | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing |
| Pages | 137-144 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Event | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing - Berkeley, CA, United States Duration: Sep 14 2009 → Sep 16 2009 |
Other
| Other | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing |
|---|---|
| Country | United States |
| City | Berkeley, CA |
| Period | 9/14/09 → 9/16/09 |
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Keywords
- Association rule mining
- Concept detection
- Multiple Correspondence Analysis (MCA)
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Software
- Electrical and Electronic Engineering
Cite this
Mining high-level features from video using associations and correlations. / Lin, Lin; Shyu, Mei-Ling.
ICSC 2009 - 2009 IEEE International Conference on Semantic Computing. 2009. p. 137-144 5298603.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Mining high-level features from video using associations and correlations
AU - Lin, Lin
AU - Shyu, Mei-Ling
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Association rule mining (ARM) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high efficiency and accuracy. Two important processes in mining the association rules for classification are rule generation and rule selection. In this paper, a novel high-level feature detection framework using the ARM technique together with the correlations among the feature-value pairs is proposed. A new association rule mining (ARM) algorithm has been developed, where the N-feature-value pairs are generated using a combined measure based on (1) the existence of the (N-1)-feature-value pairs (where N is larger than 1), (2) the correlation between different N-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intrasimilarity. The final association classification rules are selected by using the calculated harmonic mean of the similarity values. The proposed framework enables the automatic discovery and generation of the N-feature-value pair association rules from the 1-feature-value pairs for classification. Experimenting with 15 high-level features (concepts) and benchmark data sets from TRECVID, our proposed framework achieves promising performance and outperforms three other well-known classifiers (Decision Trees, Support Vector Machine, and Neural Networks) which are commonly used for performance comparison in the TRECVID community.
AB - Association rule mining (ARM) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high efficiency and accuracy. Two important processes in mining the association rules for classification are rule generation and rule selection. In this paper, a novel high-level feature detection framework using the ARM technique together with the correlations among the feature-value pairs is proposed. A new association rule mining (ARM) algorithm has been developed, where the N-feature-value pairs are generated using a combined measure based on (1) the existence of the (N-1)-feature-value pairs (where N is larger than 1), (2) the correlation between different N-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intrasimilarity. The final association classification rules are selected by using the calculated harmonic mean of the similarity values. The proposed framework enables the automatic discovery and generation of the N-feature-value pair association rules from the 1-feature-value pairs for classification. Experimenting with 15 high-level features (concepts) and benchmark data sets from TRECVID, our proposed framework achieves promising performance and outperforms three other well-known classifiers (Decision Trees, Support Vector Machine, and Neural Networks) which are commonly used for performance comparison in the TRECVID community.
KW - Association rule mining
KW - Concept detection
KW - Multiple Correspondence Analysis (MCA)
UR - http://www.scopus.com/inward/record.url?scp=73449130148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73449130148&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2009.59
DO - 10.1109/ICSC.2009.59
M3 - Conference contribution
AN - SCOPUS:73449130148
SN - 9780769538006
SP - 137
EP - 144
BT - ICSC 2009 - 2009 IEEE International Conference on Semantic Computing
ER -