{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:46:52Z","timestamp":1781106412637,"version":"3.54.1"},"reference-count":31,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,1,1]]},"abstract":"<p>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.<\/p>","DOI":"10.4018\/jmdem.2010111203","type":"journal-article","created":{"date-parts":[[2010,4,16]],"date-time":"2010-04-16T17:23:53Z","timestamp":1271438633000},"page":"37-54","source":"Crossref","is-referenced-by-count":34,"title":["Weighted Association Rule Mining for Video Semantic Detection"],"prefix":"10.4018","volume":"1","author":[{"given":"Lin","family":"Lin","sequence":"first","affiliation":[{"name":"University of Miami, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Ling","family":"Shyu","sequence":"additional","affiliation":[{"name":"University of Miami, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jmdem.2010111203-0","unstructured":"Agrawal, R., & Srikant, R. (1994). 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