Please use this identifier to cite or link to this item: https://doi.org/10.1145/1386352.1386378
Title: Probabilistic optimized ranking for multimedia semantic concept detection via RVM
Authors: Zheng, Y.-T.
Neo, S.-Y. 
Chua, T.-S. 
Tian, Q.
Keywords: High level feature detection
Learning to rank
Relevance vector machine
Issue Date: 2008
Source: Zheng, Y.-T.,Neo, S.-Y.,Chua, T.-S.,Tian, Q. (2008). Probabilistic optimized ranking for multimedia semantic concept detection via RVM. CIVR 2008 - Proceedings of the International Conference on Content-based Image and Video Retrieval : 161-168. ScholarBank@NUS Repository. https://doi.org/10.1145/1386352.1386378
Abstract: 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 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. Copyright 2008 ACM.
Source Title: CIVR 2008 - Proceedings of the International Conference on Content-based Image and Video Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/41476
ISBN: 9781605580708
DOI: 10.1145/1386352.1386378
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