On predicting rare classes with SVM ensembles in scene classification

Scene classification is an important technique to infer high-level semantic scene categories from low-level visual features. However, in the real world the positive data for many scenes may be rare, which degrades the performance of many classifiers. In this paper, we propose SVM ensembles to address the rare class problem. Various classifier combination strategies are investigated, including majority voting, sum rule, neural network gater and hierarchical SVMs. We also compare our method with two other common approaches for dealing with the rare class problem. Our experimental results show that hierarchical SVMs can achieve significantly better and more stable performance than other strategies, as well as high computational efficiency.