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CIVR
2008
Springer

Probabilistic optimized ranking for multimedia semantic concept detection via RVM

9 years 1 months ago
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
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 vector 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 theoretical...
Yantao Zheng, Shi-Yong Neo, Tat-Seng Chua, Qi Tian
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2008
Where CIVR
Authors Yantao Zheng, Shi-Yong Neo, Tat-Seng Chua, Qi Tian
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