Learning the classifier combination for image classification

Although some image features and algorithms succeed in many tasks such as scene recognition and face recognition, carefully choosing image features and classifiers are time consuming for a specific image classification task. In this paper, we propose a method that automatically combines the classifiers with probability outputs from different features. We fomulate the problem in quadric programming framework, and solve it efficiently. In addition, we proposed two classifier selection algrithms for selecting the most discriminative classifiers for the speedaccurcy trade-off. The experiment on Corel image dataset shows that our algorithm can fuse the classifiers robustly, and the classifier selection algorithm is flexible and effective.