Probabilistic optimized ranking for multimedia semantic concept detection via RVM

Yan-Tao Zheng, Shi-Yong Neo, Tat-Seng Chua, Qi Tian
2008 Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08  
We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize Support Vector Machines (SVM) to perform the detection and ranking of retrieved video shots. However, the margin maximization principle of SVM does not perform ranking optimization but merely classification error minimization. To tackle this problem, we exploit the sparse Bayesian kernel model, namely the relevance
more » ... or machine (RVM), as the classifier for semantic concept detection. Based on automatic relevance determination principle, RVM outputs the posterior probabilistic prediction of the semantic concepts. This inference output is optimal for ranking the target video shots, according to the Probabilistic Ranking Principle. The probability output of RVM on individual uni-modal features also facilitates probabilistic fusion of multi-modal evidences to minimize Bayes risk. We demonstrate both theoretically and empirically that RVM outperforms SVM for video semantic concept detection. The testings on TRECVID 07 dataset show that RVM produces statically significant improvements in MAP scores over the SVM-based methods.
doi:10.1145/1386352.1386378 dblp:conf/civr/ZhengNCT08 fatcat:66as6qbcqzdpfahxlqkcy2k6vm