Correlation-based re-ranking for semantic concept detection

Abstract

Semantic concept detection is among the most important and challenging topics in multimedia research. Its objective is to effectively identify high-level semantic concepts from low-level features for multimedia data analysis and management. In this paper, a novel re-ranking method is proposed based on correlation among concepts to automatically refine detection results and improve detection accuracy. Specifically, multiple correspondence analysis (MCA) is utilized to capture the relationship between a targeted concept and all other semantic concepts. Such relationship is then used as a transaction weight to refine detection ranking scores. To demonstrate its effectiveness in refining semantic concept detection, the proposed re-ranking method is applied to the detection scores of TRECVID 2011 benchmark data set, and its performance is compared with other state-of-the-art re-ranking approaches.