Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
ACM Computing Surveys (CSUR)
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature weighting for co-occurrence-based classification of words
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Integrated Generic Association Rule Based Classifier
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Image retrieval model based on weighted visual features determined by relevance feedback
Information Sciences: an International Journal
Technology of Information Push Based on Weighted Association Rules Mining
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Mining Allocating Patterns in One-Sum Weighted Items
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Mining Positive and Negative Weighted Association Rules from Frequent Itemsets Based on Interest
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Foundations and Trends in Information Retrieval
F-ratio Based Weighted Feature Extraction for Similar Shape Character Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Video semantic concept detection via associative classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Correlation-Based Ranking for Large-Scale Video Concept Retrieval
International Journal of Multimedia Data Engineering & Management
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
Hi-index | 0.00 |
Semantic knowledge detection of multimedia content has become a very popular research topic in recent years. The association rule mining ARM technique has been shown to be an efficient and accurate approach for content-based multimedia retrieval and semantic concept detection in many applications. To further improve the performance of traditional association rule mining technique, a video semantic concept detection framework whose classifier is built upon a new weighted association rule mining WARM algorithm is proposed in this article. Our proposed WARM algorithm is able to capture the different significance degrees of the items feature-value pairs in generating the association rules for video semantic concept detection. Our proposed WARM-based framework first applies multiple correspondence analysis MCA to project the features and classes into a new principle component space and discover the correlation between feature-value pairs and classes. Next, it considers both correlation and percentage information as the measurement to weight the feature-value pairs and to generate the association rules. Finally, it performs classification by using these weighted association rules. To evaluate our WARM-based framework, we compare its performance of video semantic concept detection with several well-known classifiers using the benchmark data available from the 2007 and 2008 TRECVID projects. The results demonstrate that our WARM-based framework achieves promising performance and performs significantly better than those classifiers in the comparison.