The nature of statistical learning theory
The nature of statistical learning theory
Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
The probability ranking principle in IR
Readings in information retrieval
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Relationship between support vector set and kernel functions in SVM
Journal of Computer Science and Technology
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Probability ranking principle via optimal expected rank
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Validity and power of t-test for comparing MAP and GMAP
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image transform bootstrapping and its applications to semantic scene classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Portfolio theory of multimedia fusion
Proceedings of the international conference on Multimedia
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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.