Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Lessons for the future from a decade of informedia video analysis research
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A probability model for combining ranks
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
AP-based borda voting method for feature extraction in TRECVID-2004
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Mixed group ranks: preference and confidence in classifier combination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Study on the combination of video concept detectors
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning automatic concept detectors from online video
Computer Vision and Image Understanding
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Rank aggregation (RA) is an important classifier combination technology for semantic concept detection (SCD) in video because modelling of semantic concepts based on multimodal representations requires effective and robust methods of classifier combination. Although many RA methods have been developed and proven workable in practice, there are few theoretical hints for devising better ones because the reasons why RA can improve the classification precision have not been thoroughly elucidated. In this work, we use the order statistics to reveal the meaning of rank and RA for classification problems and propose the Probabilistic Model Supported Rank Aggregation (PMSRA) framework, which not only provides a probabilistic interpretation of why RA may be good for classification but also serves as a possible guide to new RA methods. Moreover, we apply the principle of Bayesian decision to the PMSRA framework to develop a new RA method, i.e. the Bayesian PMSRA. The effectiveness and robustness of our method have been further collaborated by the experimental results of incremental RA for SCD in video on the TRECVID 2005's dataset and an artificial dataset.