基于线性SVM的级联检测算子的构建

本文介绍了一种基于支持向量机(SVM)理论的线性级联式分类器,用于解决较复杂目标的快速检测问题.该分类器由若干个线性SVM分类器组成,结合了级联分类器和SVM理论的优点,给出了级联结构中的每个节点的约束最优化模型,使得每个节点都有较高的正样本检测率和适当的负样本错检率.实验结果表明,与经典非线性svM分类器相比,这种分类器在保持SCM较强泛化性能的优点的同时,在检测效率方面更是具有明显的优势.     

一种基于线性SVM的级联分类器的构造方法

为了对目标进行快速的检测,提出了一种新的基于支持向量机的级联式分类器的构造方法。该级联分类器由若干个线性SVM弱分类器构成,结构简单,分类时间极快。针对级联结构中的每个节点的训练给出了一个新的SVM框架下的二次规划模型,这使得每个节点都有较高的正样本检测率和适当的负样本错检率。实际的实验结果表明,与经典非线性SVM分类器相比,这种分类器在保持SVM较强泛化性能的优点的同时,在检测效率方面更是具有明显的优势。     

级联SVM和分类器融合的人脸性别识别方法

传统的支持向量机（Support Vector Machines,SVM）在面对大样本训练问题时,其样本数量会受到内存的限制。因此,提出一种基于级联SVM和分类器融合的人脸图像性别识别方法。级联SVM分类器可以通过设定阈值将识别难易程度不同的样本分成若干层次来进行训练;同时,在级联的每一层上,为了降低分类器在识别过程中受各种因素的影响,对不同特征维数下得到的最优分类器进行融合,通过融合减小误差,使中性的人脸样本有更明确的分类。在同一硬件条件下的实验结果表明,单层SVM最多只能训练7万样本,而四层级联SVM训练样本数可达12万以上,相应的识别率也从单层融合前的96.7%上升至四层融合后的99.1%。     

利用级联SVM的人体检测方法

从图像中检测出人体是计算机视觉应用中的关键步骤。通过一个由简到繁的级联线性SVM分类器将级联拒绝的机制与梯度方向直方图特征相结合,实现了一个准确和快速的人体检测器,整个检测器由级联的线性SVM分类器组成。实验结果表明,在保持Dalal算法检测准确性的同时,大幅的提高了检测速度,每秒平均可以处理12帧左右的320×240的图像。     

基于级联支持向量机的人脸图像性别识别

提出一种由若干个支持向量机(SVM)分类器串连而成的级联SVM算法,用于人脸图像性别识别。该算法把容易被前一层分类器分类的训练样本过滤掉,将难度较高的训练样本重新组织起来训练新一层的分类器。结合级联分类器和SVM理论的优势,在训练过程中能够使用更多的样本,具有更好的识别性能。在同一硬件实验条件下的实验结果表明,单层SVM最多只能训练7万样本,而四层级联SVM的训练样本数可达12万以上,相应的识别率也从96.6%上升至98.4%。     

基于AdaBoost-SVM级联分类器的行人检测

针对实时行人检测中AdaBoost级联分类算法存在的问题,改进AdaBoost级联分类器的训练算法,提出了Ada-Boost-SVM级联分类算法,它结合了AdaBoost和SVM两种算法的优点.对自定义样本集和PET图像库进行行人检测实验,实验中选择固定大小的窗口作为候选区域并利用类Haar矩形特征进行特征提取,通过AdaBoost-SVM级联分类器进行分类.实验结果表明AdaBoost-SVM级联分类器的分类器准确率达到99.5％,误报率低于0.05％,优于AdaBoost级联分类器,训练时间要远远小于SVM分类器.     

多特征融合的道路车辆检测方法

通过改进基于Haar-like特征和Adaboost的级联分类器,提出一种融合Haar-like特征和HOG特征的道路车辆检测方法。在传统级联分类器的Harr-like特征基础上引入HOG特征;为Haar-like特征和HOG特征分别设计不同形式的弱分类器,对每一个特征进行弱分类器的训练,用Gentle Adaboost算法代替Discrete Adaboost算法进行强分类器的训练;在级联分类器的最后几层上使用Adaboost算法挑选出来的特征组成特征向量训练SVM分类器。实验结果表明所提出的方法能有效检测道路车辆。     

聚合支持向量机分类器的行人检测方法

针对支持向量机分类器的行人检测方法采用欠采样方法,存在正负行人比例不平衡造成的准确率不高问题,结合欠采样和EasyEnsemble方法,提出一种聚合支持向量机（Ensemble SVM）分类器的行人检测方法。随机选择负样本作为初始训练样本,并将其划分为与正样本集均衡的多个子负样本集,构建平衡子训练集,线性组合成EasyEnsemble SVM分类器;利用该分类器对负样本进行分类判断,将误判样本作为难例样本,重新划分构建新的平衡子训练集,训练子分类器,结合EasyEnsemble SVM分类器,得到Ensemble SVM分类器行人检测方法。在INRIA行人数据集上的实验表明,该方法在检测速度和检测率上都优于经典的SVM行人检测算法。     

基于Adaboost和SVM的人头实时检测

针对复杂场景图像中的人头检测问题,提出一种Adaboost与支持向量机(SVM)相结合的检测算法.该算法重点对Adaboost特征进行了改进,用Adaboost对人头进行快速检测,并引入级联的SVM分类器对Adaboost检测结果进行逐级筛选,从而实现对人头的精确检测.实验表明,该方法降低了Adaboost运算复杂度,提高了特征分类能力,引入级联SVM分类器在保证高检测率的同时,降低了误检率,对复杂场景具有较强的鲁棒性.     

复杂背景下人眼的快速定位

采用基于改进Adaboost算法的级联式人脸和人眼分类器检测人脸和眼睛的候选位置,再用支持向量机（SVM）分类器验证并确定人眼的最佳位置;实现了在复杂背景图像中快速、准确的眼睛定位.与传统的Adaboost算法相比,改进的Adaboost算法构建分类器所需的特征数目大大减少,提高了眼睛定位速度;同时利用人脸几何特征的SVM分类器验证,提高了定位精度.实验结果表明该算法具有很好的精确性和实时性.     

SVM based adaptive learning method for text classification from positive and unlabeled documents

Automatic text classification is one of the most important tools in Information Retrieval. This paper presents a novel text classifier using positive and unlabeled examples. The primary challenge of this problem as compared with the classical text classification problem is that no labeled negative documents are available in the training example set. Firstly, we identify many more reliable negative documents by an improved 1-DNF algorithm with a very low error rate. Secondly, we build a set of classifiers by iteratively applying the SVM algorithm on a training data set, which is augmented during iteration. Thirdly, different from previous PU-oriented text classification works, we adopt the weighted vote of all classifiers generated in the iteration steps to construct the final classifier instead of choosing one of the classifiers as the final classifier. Finally, we discuss an approach to evaluate the weighted vote of all classifiers generated in the iteration steps to construct the final classifier based on PSO (Particle Swarm Optimization), which can discover the best combination of the weights. In addition, we built a focused crawler based on link-contexts guided by different classifiers to evaluate our method. Several comprehensive experiments have been conducted using the Reuters data set and thousands of web pages. Experimental results show that our method increases the performance (F₁-measure) compared with PEBL, and a focused web crawler guided by our PSO-based classifier outperforms other several classifiers both in harvest rate and target recall.     

基于Gabor变换和Adaboost算法的人体目标检测分类器

针对在图像中检测人体目标,提出一种基于Gabor变换和Adaboost算法的检测方法.首先利用二维Gabor小波变换进行特征提取,然后利用Adaboost算法对Gabor特征进行选取并训练强分类器.为了提高检测精度,提出采用单一正样本集合与多个负样本集合分别进行训练,形成多个强分类器级联的层级检测分类器.实验结果表明了该方法的有效性,同时显示该方法须与其它辅助手段相结合,才能提高检测的实时性.     

基于SVM主动学习技术的PU文本分类

PU文本分类(以正例和未标识实例集训练分类器的分类方法)关键在于从U(未标识实例)集中提取尽可能多的可靠反例,然后在正例与可靠反例的基础上使用机器学习的方法构造有效分类器,而已有的方法可靠反例的数量少或不可靠,同样构造的分类器也精度不高,基于SVM主动学习技术的PU文本分类算法提出一种利用SVM与改进的Rocchio分类器进行主动学习的PU文本分类方法,并通过spy技术来提高SVM分类器的准确度,解决某些机器学习中训练样本获取代价过大,尤其是反例样本较难获取的实际问题。实验表明,该方法比目前其它的主动学习方法及面向PU的文本分类方法具有更高的准确率和召回率。     

Training Effective Node Classifiers for Cascade Classification

Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al. (linear asymmetric classifier for cascade detectors, 2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.     

基于PU学习的链接预测方法

基于分类的链接预测方法中,由于链接未知节点对的大规模性与不确定性,选择可靠负例成为构造链接预测分类器的难点问题.为此,文中提出基于正例和无标识样本(PU)学习的链接预测方法.首先,提取节点对的拓扑信息以构造样本集.再利用社区结构确定候选负例的分布,基于分布进行多次欠采样,获得多个候选负例子集,集成多个负例集与正例集中构建的分类器选择可靠负例.最后基于正例与可靠负例构造链接预测分类器.在4个网络数据集上的实验表明文中方法预测结果较优.     

Fast and Robust Face Detection Using Evolutionary Pruning

Face detection task can be considered as a classifier training problem. Finding the parameters of the classifier model by using training data is a complex process. To solve such a complex problem, evolutionary algorithms can be employed in cascade structure of classifiers. This paper proposes evolutionary pruning to reduce the number of weak classifiers in AdaBoost-based cascade detector, while maintaining the detection accuracy. The computation time is proportional to the number of weak classifiers and, therefore, a reduction in the number of weak classifiers results in an increased detection speed. Three kinds of cascade structures are compared by the number of weak classifiers. The efficiency in computation time of the proposed cascade structure is shown experimentally. It is also compared with the state-of-the-art face detectors, and the results show that the proposed method outperforms the previous studies. A multiview face detector is constructed by incorporating the three face detectors: frontal, left profile, and right profile.     

Automatic recognition of serial numbers in bank notes

This paper presents a new topic of automatic recognition of bank note serial numbers, which will not only facilitate the prevention of forgery crimes, but also have a positive impact on the economy. Among all the different currencies, we focus on the study of RMB (renminbi bank note, the paper currency used in China) serial numbers. For evaluation, a new database NUST-RMB2013 has been collected from scanned RMB images, which contains the serial numbers of 35 categories with 17,262 training samples and 7000 testing samples in total. We comprehensively implement and compare two classic and one newly merged feature extraction methods (namely gradient direction feature, Gabor feature, and CNN trainable feature), four different types of well-known classifiers (SVM, LDF, MQDF, and CNN), and five multiple classifier combination strategies (including a specially designed novel cascade method). To further improve the recognition accuracy, the enhancements of three different kinds of distortions have been tested. Since high reliability is more important than accuracy in financial applications, we introduce three rejection schemes of first rank measurement (FRM), first two ranks measurement (FTRM) and linear discriminant analysis based measurement (LDAM). All the classifiers and classifier combination schemes are combined with different rejection criteria. A novel cascade rejection measurement achieves 100% reliability with less rejection rate compared with the existing methods. Experimental results show that MQDF reaches the accuracy of 99.59% using the gradient direction feature trained with gray level normalized data; the cascade classifier combination achieves the best performance of 99.67%. The distortions have been proved to be very helpful because the performances of CNNs boost at least 0.5% by training with transformed samples. With the cascade rejection method, 100% reliability has been obtained by rejecting 1.01% test samples.     

基于MapReduce和上采样的两类非平衡大数据集成分类

提出了一种基于MapReduce和上采样的两类非平衡大数据分类方法,该方法分为5步：（1）对于每一个正类样例,用MapReduce寻找其异类最近临;（2）在两个样例点之间的直线上生成若干个正类样例;（3）以新的正类样例子集的大小为基准,将负类样例随机划分为若干子集;（4）用负类样例子集和正类样例子集构造若干个平衡数据子集;（5）用平衡数据子集训练若干个分类器,并对训练好的分类器进行集成。在5个两类非平衡大数据集上与3种相关方法进行了实验比较,实验结果表明本文提出的优于这3种方法。     

Cascade Generalization

Using multiple classifiers for increasing learning accuracy is an active research area. In this paper we present two related methods for merging classifiers. The first method, Cascade Generalization, couples classifiers loosely. It belongs to the family of stacking algorithms. The basic idea of Cascade Generalization is to use sequentially the set of classifiers, at each step performing an extension of the original data by the insertion of new attributes. The new attributes are derived from the probability class distribution given by a base classifier. This constructive step extends the representational language for the high level classifiers, relaxing their bias. The second method exploits tight coupling of classifiers, by applying Cascade Generalization locally. At each iteration of a divide and conquer algorithm, a reconstruction of the instance space occurs by the addition of new attributes. Each new attribute represents the probability that an example belongs to a class given by a base classifier. We have implemented three Local Generalization Algorithms. The first merges a linear discriminant with a decision tree, the second merges a naive Bayes with a decision tree, and the third merges a linear discriminant and a naive Bayes with a decision tree. All the algorithms show an increase of performance, when compared with the corresponding single models. Cascade also outperforms other methods for combining classifiers, like Stacked Generalization, and competes well against Boosting at statistically significant confidence levels.