Learning from error: A two-level combined model for image classification

We propose an error learning model for image classification. Motivated by the observation that classifiers trained using local grid regions of the images are often biased, i.e., contain many classification error, we present a two-level combined model to learn useful classification information from these errors, based on Bayes rule. We give theoretical analysis and explanation to show that this error learning model is effective to correct the classification errors made by the local region classifiers. We conduct extensive experiments on benchmark image classification datasets, promising results are obtained.