Direct mining co-occurrence features for visual recognition: A branch and bound method

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 and scene recognition, the discovery of discriminative co-occurrence features is usually a computational 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 feature co-occurrence, we propose a novel branch-and-bound 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 error is minimized by the discovered co-occurrence features in each boosting step. Experiments on the benchmark datasets and scene recognition dataset validate the advantages of our proposed method.