Efficient mining of optimal and/or patterns for visual recognition

The co-occurrence features are the composition of base features that have more discriminative power than individual base features. Although they show promising performance in visual recognition applications such as object, scene, and action recognition, the discovery of optimal co-occurrence features is usually a computationally demanding task. Unlike previous feature mining methods that fix the order of the co-occurrence features or rely on a two-stage frequent pattern mining to select the optimal co-occurrence feature, we propose a novel branch-and-bound search-based co-occurrence feature mining algorithm that can directly mine both optimal conjunctions (AND) and disjunctions (OR) of individual features at arbitrary orders simultaneously. This feature mining process is integrated into a multi-class boosting framework Adaboost.MH such that the weighted training error is minimized by the discovered co- occurrence features in each boosting step. Experiments on UCI benchmark datasets, the scene recognition dataset, and the action recognition dataset validate both the effectiveness and efficiency of our proposed method.