I. Introduction
As one of the widely used ensemble-based learning algorithms, boosting has achieved great successes on various computer vision applications (e.g., object detection [1], object classification [2], object tracking [3], image retrieval [4]) since the first real-time face detector [5]. The strategy of boosting is to progressively combine weak classifiers to form a strong classifier, instead of trying to learn a single complex classifier. During the training process, boosting pays much more attention to those misclassified training samples [6]. As a general and effective learning scheme, boosting has been deeply studied in theory and has been adopted to deal with a wide variety of problems in pattern recognition. Briefly, two explanations are proposed to understand the success of boosting: functional gradient view [7] and margin-based optimization [8]. Given the loss function of boosting, the former finds the best weak classifier by gradient descent in each step, and its combination weight can be obtained simultaneously. The latter tries to formulate this problem as a margin-based optimization problem as support vector machine (SVM) does. A complete review of boosting algorithms is presented in [9].