Abstract

We address the problem of complicated event categorization from a large dataset of videos “in the wild”, where multiple classifiers are applied independently to evaluate each video with a ‘likelihood’ score. The core contribution of this paper is a local expert forest model for meta-level score fusion for event detection under heavily imbalanced class distributions. Our motivation is to adapt to performance varia- tions of the classifiers in different regions of the score space, using a divide-and-conquer technique. We propose a novel method to partition the likelihood-space, being sensitive to local label distributions in im- balanced data, and train a pair of locally optimized experts each time. Multiple pairs of experts based on different partitions (‘trees’) form a ‘forest’, balancing local adaptivity and over-fitting of the model. As a re- sult, our model disregards classifiers in regions of the score space where their performance is bad, achieving both local source selection and fu- sion. We experiment with the TRECVID Multimedia Event Detection (MED) dataset, detecting 15 complicated events from around 34k video clips comprising more than 1000 hours, and demonstrate superior per- formance compared to other score-level fusion methods.