Study on the combination of video concept detectors

This paper studies the combination of video concept detectors with a labeled fusion set. We point out that the computational cost of the grid search for fusion weights increases exponentially with the number of detectors, and it is thus infeasible when dealing with a large number of detectors. To avoid the difficulty, we adopt incremental fusion approach, i.e., in each round two detectors are combined and hence only 1-dimensional grid search is needed. We propose a Bottom-Up Incremental Fusion (BUIF) method which keeps selecting the detectors with lowest performance for combination. We conduct experiments on TRECVID benchmark dataset for 39 concepts with 38 detection methods. Ten different fusion strategies are compared, and empirical results have demonstrated the superiority of the proposed incremental fusion approach.