Probabilistic model supported rank aggregation for the semantic concept detection in video

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.