Weighted Association Rule Mining for Video Semantic Detection

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.