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Fast asymmetric learning for cascade face detection 总被引:1,自引:0,他引:1
Wu J Brubaker SC Mullin MD Rehg JM 《IEEE transactions on pattern analysis and machine intelligence》2008,30(3):369-382
A cascade face detector uses a sequence of node classifiers to distinguish faces from non-faces. This paper presents a new approach to design node classifiers in the cascade detector. Previous methods used machine learning algorithms that simultaneously select features and form ensemble classifiers. We argue that if these two parts are decoupled, we have the freedom to design a classifier that explicitly addresses the difficulties caused by the asymmetric learning goal. There are three contributions in this paper. The first is a categorization of asymmetries in the learning goal, and why they make face detection hard. The second is the Forward Feature Selection (FFS) algorithm and a fast pre- omputing strategy for AdaBoost. FFS and the fast AdaBoost can reduce the training time by approximately 100 and 50 times, in comparison to a naive implementation of the AdaBoost feature selection method. The last contribution is Linear Asymmetric Classifier (LAC), a classifier that explicitly handles the asymmetric learning goal as a well-defined constrained optimization problem. We demonstrated experimentally that LAC results in improved ensemble classifier performance. 相似文献
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FloatBoost learning and statistical face detection 总被引:14,自引:0,他引:14
A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported. 相似文献
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基于"遗传+变异"模式,提出继承式集成学习方法框架,它可以训练出四种不同形式的层叠分类器。除了基于"无遗传"模式的基本层叠分类器与基于"全部遗传"模式的嵌入式层叠分类器两种传统方法之外,还有基于"部分遗传+部分变异"模式的特征继承层叠分类器与弱分类器继承层叠分类器。虽然后两种层叠分类器都有一定的继承代价,但是其拟合性更好,可以更好地均衡收敛速度和扩展性能,其综合性能优于传统方法。基于RAB、GAB算法与LUT弱分类器的正面直立人脸检测实验结果表明了新的继承式集成学习方法的有效性。 相似文献
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分析了模糊集理论运用于人脸检测的可行性,采用Haar矩形特征和隶属度函数对样本集进行训练,运用特征集的熵和AdaBoost算法选取适当的弱分类器,并构建了分发型人脸检测器。检测时,对于不像人脸的子窗口通过靠前的结构简单的强分类器快速将其淘汰掉;对于像人脸的子窗口,根据其与人脸的相似程度,由分发器动态地选择后面的强分类器进行判定。在MIT+CMU的正面人脸图片集中进行了测试,实验结果表明,此检测器在检测性能降低不大的情况下,可以有效地提高检测效率。 相似文献
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Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases. 相似文献
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针对现有基于粒子群(PSO)策略的Adaboost人脸检测方法没有考虑到PSO容易陷入局部最优且后期收敛速度较慢的问题,提出一种改进的Adaboost人脸检测方法。该方法将自适应逃逸粒子群(AEPSO)~I入传统Adaboost人脸检测中,利用粒子表达Haar-Like矩形特征,从而将特征选择和分类器构建转化为AEPSO问题进行解决。基于Matlab仿真实验的结果表明,改进后的方法具有较好的检测性能。 相似文献
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《Computer Vision and Image Understanding》2007,105(2):99-111
Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-view face detection. Specifically, a recursive nonparametric discriminant analysis (RNDA) method is presented. The RNDA relaxes Gaussian assumptions of Fisher discriminant analysis (FDA), and it can handle more general class distributions. RNDA also improves the traditional nonparametric discriminant analysis (NDA) by alleviating its computational complexity. The resulting RNDA features provide better accuracy than the commonly used Haar features in detecting objects of complex shapes. Histograms of extracted features are learned to represent class distributions and to construct probabilistic classifiers. RNDA features are subsequently learned and combined with AdaBoost to form a multi-view face detector. The method is applied to both multi-view face and eye detection, and experimental results demonstrate improved performance over existing methods. 相似文献
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AdaBoost算法是一种典型的集成学习框架,通过线性组合若干个弱分类器来构造成强学习器,其分类精度远高于单个弱分类器,具有很好的泛化误差和训练误差。然而AdaBoost
算法不能精简输出模型的弱分类器,因而不具备良好的可解释性。本文将遗传算法引入AdaBoost算法模型,提出了一种限制输出模型规模的集成进化分类算法(Ensemble evolve classification algorithm for controlling the size of final model,ECSM)。通过基因操作和评价函数能够在AdaBoost迭代框架下强制保留物种样本的多样性,并留下更好的分类器。实验结果表明,本文提出的算法与经典的AdaBoost算法相比,在基本保持分类精度的前提下,大大减少了分类器数量。 相似文献
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Gender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy. 相似文献
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AdaBoost-based algorithm for network intrusion detection. 总被引:1,自引:0,他引:1
Weiming Hu Wei Hu Steve Maybank 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2008,38(2):577-583
Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data. 相似文献
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提出了一种基于Adaboost算法和CART算法结合的分类算法。以特征为节点生成CART二叉树,用CART二叉树代替传统Adaboost算法中的弱分类器,再由这些弱分类器生成强分类器。将强分类器对数字样本和人脸样本分类,与传统Adaboost算法相比,该方法的错误率分别减少20%和86.5%。将分类器应用于目标检测上,实现了对这两种目标的快速检测和定位。结果表明,改进算法既减小了对样本分类的错误率,又保持了传统Adboost算法对目标检测的快速性。 相似文献
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随着电力系统直升飞机巡线的不断发展与应用,对于输电线路关键部件的检测与识别越来越受到图像处理工作者的青睐。提出了一种利用3D模型制作训练样本及Ada Boost算法实现的航拍图像绝缘子自动检测方法。根据绝缘子3D模型图像的空间结构特征,提出了能反映这些结构的Haar矩形特征,从中挑选对绝缘子航拍图像有最好区分的特征构成弱分类器,再组合生成强分类器。使用正负样本图像训练后,由强分类器级联组成了一个多层分类器系统。实验结果表明,该方法有效地提升了绝缘子的识别效果,为后续的故障检测工作提供了良好的铺垫。 相似文献
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针对复杂场景中运动目标较难定位的问题,提出一种结合纹理和颜色特征的AdaBoost目标跟踪算法.首先在线训练一个弱分类器的集合区分目标和背景;然后,通过AdaBoost将集合中的各弱分类器组合成一个强分类器,用于标定下一帧中各像素的类别属性,并生成置信图;最后,在置信图中用Mean Shift算法定位目标的中心.实验结果表明,该算法在光照变化、目标自身发生形变和遮挡的情况下,能准确地对目标进行跟踪. 相似文献
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Wen-Chang Cheng Ding-Mao Jhan 《Engineering Applications of Artificial Intelligence》2013,26(3):1016-1028
In this paper, we propose a cascade classifier combining AdaBoost and support vector machine, and applied this to pedestrian detection. The pedestrian detection involved using a window of fixed size to extract the candidate region from left to right and top to bottom of the image, and performing feature extractions on the candidate region. Finally, our proposed cascade classifier completed the classification of the candidate region. The cascade-AdaBoost classifier has been successfully used in pedestrian detection. We have improved the initial setting method for the weights of the training samples in the AdaBoost classifier, so that the selected weak classifier would be able to focus on a higher detection rate other than accuracy. The proposed cascade classifier can automatically select the AdaBoost classifier or SVM to construct a cascade classifier according to the training samples, so as to effectively improve classification performance and reduce training time. In order to verify our proposed method, we have used our extracted database of pedestrian training samples, PETs database, INRIA database and MIT database. This completed the pedestrian detection experiment whose result was compared to those of the cascade-AdaBoost classifier and support vector machine. The result of the experiment showed that in a simple environment involving campus experimental image and PETs database, both our cascade classifier and other classifiers can attain good results, while in a complicated environment involving INRA and MIT database experiments, our cascade classifier had better results than those of other classifiers. 相似文献