AdaBoost has been verified to be proficient in processing images rapidly while attaining high detection rate in face detection. The speed of AdaBoost in face detection is demonstrated in [1], where the detection can be performed in 15 frames per second. The robust speediness and the high accuracy in tracing the target objects have enable AdaBoost to be successful in classification problems. In ...

Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. The AdaBoost algorithm of Freund and Schapire was the first practical boosting algorithm, and remains one of the most widely used and studied, with applications in numerous fields. This chapter aims to review some of the many perspec...

We propse a new boosting algorithm that mends some of the problems that have been detected in the so far most successful boosting algorithm, AdaBoost due to Freund and Schapire [FS97]. These problems are: (1) AdaBoost cannot be used in the boosting by filtering framework, and (2) AdaBoost does not seem to be noise resistant. In order to solve them, we propose a new boosting algorithm MadaBoost ...

For color images in a complex background, we cannot be able to detect faces quickly. So we put forward an algorithm, which is based on skin color feature and the improved AdaBoost algorithm. First, through the skin color detection to excluding large amounts of complex background of non-face, after that define the face candidate regions. Besides, when the image is darkness, we will increase the ...

We investigate AdaBoost and bipartite version of RankBoost abilities to minimize AUC and its application for score level fusion in multimodal biometric systems. To do this, we customize two methods of weak learner training. Empirical results show comparable AUC for AdaBoost and RankBoost.B which previously was addressed theoretically. We demonstrate exhaustive results among state of the art cla...

Face Detection is an essential first step of the face recognition process, and has significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. This paper deeply researches and analysis the principle, merits and demerits of the classic AdaBoost face detection algorithm and ASM algorithm based on point distribut...

Boosting algorithms with l1-regularization are of interest because l1 regularization leads to sparser composite classifiers. Moreover, Rosset et al. have shown that for separable data, standard lpregularized loss minimization results in a margin maximizing classifier in the limit as regularization is relaxed. For the case p = 1, we extend these results by obtaining explicit convergence bounds o...

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar f...

The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11–21] show that AdaBoost with strong component...

Support Vector Machines (SVMs) and Adaptive Boosting (AdaBoost) are two successful classification methods. They are essentially the same as they both try to maximize the minimal margin on a training set. In this work, we present an even platform to compare these two learning algorithms in terms of their test error, margin distribution and generalization power. Two basic models of polynomials an...