一种基于稀疏编码的多核学习图像分类方法

 本文提出一种基于稀疏编码的多核学习图像分类方法.传统稀疏编码方法对图像进行分类时,损失了空间信息,本文采用对图像进行空间金字塔多划分方式为特征加入空间信息限制.在利用非线性SVM方法进行图像分类时,空间金字塔的各层分别形成一个核矩阵,本文使用多核学习方法求解各个核矩阵的权重,通过核矩阵的线性组合来获取能够对整个分类集区分能力最强的核矩阵.实验结果表明了本文所提出图像分类方法的有效性和鲁棒性.对Scene Categories场景数据集可以达到83.10%的分类准确率,这是当前该数据集上能达到的最高分类准确率.     

基于测度优化Laplacian SVM的中文指代消解方法

相比于传统的基于半监督学习的指代消解方法,Laplacian SVM（Support Vector Machine）能有效的挖掘已标注样本和未标注样本的相似性和关联性,更好的推导模型的分类边界。而传统Laplacian SVM采用欧式距离度量样本之间的距离,使得异类样本之间的相似性可能过大,不利于样本的准确分类。对此,提出一种基于数据驱动学习最优测度Laplacian SVM算法以解决中文指代消解语料不足的问题。该方法通过优化样本对之间的相似性约束条件和引入Fisher判别项,增大同类样本间的相似性,并突出强判别能力的特征。此外,提出核嵌入的测度优化方法将以上线性测度优化推广到非线性空间,有利于Laplacian SVM利用核函数实现非线性分类。在ACE2005中文语料库上的测评结果表明,所提出测度优化的Laplacian SVM（包括线性和核嵌入两种形式）的方法只需少量标注样本就可以获得与经典的有监督学习模型相当甚至更好的消解性能,同时也优于其他传统的半监督学习方法。     

应用Gabor小波和支持向量机的纹理分类

针对现有纹理分类算法的局限性,提出了一种基于Gabor小波和支持向量机的纹理分类算法.首先提取纹理Gabor分解后各子带的均值和方差作为特征向量,进而利用支持向量机算法实现分类.实验结果表明,与传统的分类方法相比,Gabor小波和支持向量机相结合能有效地提高分类正确率.     

一种基于支持向量机的含噪语音的清/浊/静音分类的新方法

本文将支持向量机(SVM)方法应用于语音信号的清/浊/静音检测中,提出并验证了一种在各种信噪比等级下将语音信号有效地分为清音、浊音和静音三类信号的新型分类算法.首先,在高信噪比情况下,本文采用了G.729B VAD中的四个差分参数作为SVM分类器的输入特征参数,进行了静音分类的对比实验,得到了优于G.729B VAD和BP神经网络传统算法的实验结果,说明引入这种机器学习方法做语音分类是可行的,并分析讨论了在核函数不同的情况下支持向量机在实验中所表现出的性能.其次,又讨论了在低信噪比条件下,如何通过对含噪语音建立统计模型,提取对噪音免疫的统计特征参数,并给出了一种对时变背景噪声自适应的估计方法.最后,通过在不同噪音环境下的对比实验结果,验证了本文所提出的算法在中低信噪比情况下的分类性能要优于其他传统算法.     

基于核Fisher判别字典学习的稀疏表示分类

在基于稀疏表示分类的模式识别中,字典学习(DL) 可以为稀疏表示获得更为精简的数据表示。最近的基于Fisher判别的字典学习(FDDL)可以学 习到更加判别的稀疏字典,使得稀疏表示分类具有很强的识别性能。核空间变换可以学习到 非线性结构信息,这对判别分类非常有用。为了充分利用 核空间特性以学习更加判别的稀疏字典来提升最终的识别性能,在FDDL的基础上,提出了两 种核化的稀疏表示DL方法。首先原始训练数据被投影到高维核空间,进行基于Fisher 判别的核稀 疏表示DLFDKDL;其次在稀疏系数上附加核Fisher约束,进行基于核Fisher判别的核稀疏表 示DL(KFDKDL),使得所学习的字典具有更强的判别能力。在多个公开的图像数据库上的稀疏 表示分类实验结果验证了所提出的FDKDL和KFDKDL方法的有效性。     

Coral reef image classification employing Improved LDP for feature extraction

This paper presents a scheme for feature extraction that can be applied for classification of corals in submarine coral reef images. In coral reef image classification, texture features are extracted using the proposed Improved Local Derivative Pattern (ILDP). ILDP determines diagonal directional pattern features based on local derivative variations which can capture full information. For classification, three classifiers, namely Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN) with four distance metrices, namely Euclidean distance, Manhattan distance, Canberra distance and Chi-Square distance, and Support Vector Machine (SVM) with three kernel functions, namely Polynomial, Radial basis function, Sigmoid kernel are used. The accuracy of the proposed method is compared with Local Binary pattern (LBP), Local Tetra Pattern (LTrP), Local Derivative Pattern (LDP) and Robust Local Ternary Pattern (RLTP) on five coral data sets and four texture data sets. Experimental results indicate that ILDP feature extraction method when tested with five coral data sets, namely EILAT, RSMAS, EILAT2, MLC2012 and SDMRI and four texture data sets, namely KTH-TIPS, UIUCTEX, CURET and LAVA achieves the highest overall classification accuracy, minimum execution time when compared to the other methods.     

Robust discriminative extreme learning machine for relevance feedback in image retrieval

Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image features always make the RF time-consuming; (2) the model of SVM is not discriminative, because labels of image features are not sufficiently exploited. To solve above problems, we proposed robust discriminative extreme learning machine (RDELM) in this paper. RDELM involved both robust within-class and between-class scatter matrices to enhance the discrimination capacity of ELM for RF. Furthermore, an angle criterion dimensionality reduction method is utilized to extract the discriminative information for RDELM. Experimental results on four benchmark datasets (Corel-1K, Corel-5K, Corel-10K and MSRC) illustrate that our proposed RF method in this paper achieves better performance than several state-of-the-art methods.     

基于人工蜂群算法的支持向量机参数优化及应用

为了解决常用的支持向量机(SVM)参数优化方法在寻优过程不同程度的陷入局部最优解的问题,提出一种基于人工蜂群(ABC)算法的SVM参数优化方法。将SVM的惩罚因子和核函数参数作为食物源位置,分类正确率作为适应度,利用ABC算法寻找适应度最高的食物源位置。利用4个标准数据集,将其与遗传(GA)算法、蚁群(ACO)算法、标准粒子群(PSO)算法优化的SVM进行性能比较,结果表明,本文方法能克服局部最优解,获得更高的分类正确率,并在小数目分类问题上有效降低运行时间。将本文方法运用到计算机笔迹鉴别,对提取的笔迹特征进行分类,与GA算法、ACO算法、PSO算法优化的SVM相比,得到了更高的分类正确率。     

一种基于二维局部二值模式的纹理图像分类方法

局部二值模式（Local Binary Pattern,LBP）在纹理分类中受到越来越多的关注,传统的基于局部二值模式的图像识别方法在LBP直方图统计时仅仅考虑到LBP模式值本身的数量统计,却忽略了模式值之间的相关性.针对这一问题,本文提出一种二维局部二值模式（Two Dimensional Local Binary Pattern,2DLBP）方法,并用于纹理图像识别.首先以旋转不变均匀LBP特征图为基础,引入滑动窗口和LBP模式对的概念,统计LBP模式图的上下文信息,构造出2DLBP特征;然后改变LBP中的半径参数,构造图像的多分辨率2DLBP特征,并利用支持向量机（SVM）的分类方法进行纹理分类;最后选取Brodatz、CUReT、UIUC、FMD四个公开纹理库分别进行纹理分类测试.理论验证表明该方法具有良好的通用性,可以与LBP的其他变型结合成为新的图像特征构造方法.同时,实验结果表明,本文提出方法具有较好的纹理图像分类能力.     

基于统计几何的纹理特征提取及分类算法

纹理识别是计算机视觉领域一个重要的课题,本文研究了统计几何特征(SGF)纹理分析方法并与向量机结合构建分类系统。对支持向量机(SVM)的多分类方法的实现,构建了粗分类和细分类相结合的多分类器,实现了纹理图像的准确划分,为有效纹理特征的表示奠定了基础。本文对统计几何特征提取方法进行了研究,利用图像函数图来进行纹理描述,使用一个可变的阈值把一幅灰度纹理图像切割成一系列二进制图像,由二进制图像的连通域、几何拓扑属性推导纹理描述特征。实验结果表明,统计几何特征具有非常强的纹理描述能力,同时能够克服图像的旋转。     

A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (Az = 0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (Az = 0.80).     

SVD-based modeling for image texture classification using wavelet transformation.

This paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition. The probability density function of the singular values of wavelet transformation coefficients of image textures is modeled as an exponential function. The model parameter of the exponential function is estimated using maximum likelihood estimation technique. Truncation of lower singular values is employed to classify textures in the presence of noise. Kullback-Leibler distance (KLD) between estimated model parameters of image textures is used as a similarity metric to perform the classification using minimum distance classifier. The exponential function permits us to have closed-form expressions for the estimate of the model parameter and computation of the KLD. These closed-form expressions reduce the computational complexity of the proposed approach. Experimental results are presented to demonstrate the effectiveness of this approach on the entire 111 textures from Brodatz database. The experimental results demonstrate that the proposed approach improves recognition rates using a lower number of parameters on large databases. The proposed approach achieves higher recognition rates compared to the traditional sub-band energy-based approach, the hybrid IMM/SVM approach, and the GGD-based approach.     

离散傅里叶变换和组合能量熵的纹理图像分析

鉴于纹理特征对于图像分类的良好性能,提出了结合离散傅里叶变换和排列组合熵的纹理特征分析方法.利用主成分分析方法对特征向量进行降维,再采用支持向量机方法对纹理图像进行分类,取得了较好的效果.     

一种改进的支持向量机模式分类方法

支持向量机(Support Vector Machine,SVM)是在统计学习理论基础上发展起来的一种新的机器学习方法,已成为目前研究的热点,并在模式识别领域有了广泛的应用.首先分析了支持向量机原理,随后引入一种改进的径向基核函数,在此基础上,提出了一种改进核函数的SVM模式分类方法.与基于IRIS数据,进行了计算机仿真实验,与基干模糊k-近邻的模式分类仿真结果比较,结果表明改进的SVM方法分类性能比模糊k-近邻算法(Fuzzy k-Nearest Neighbor,FKNN)的分类性能更好,运算时间更短,更易于实时实现.     

基于SVM混合核的遥感图像变化检测

针对由实际遥感地物类型难以确定导致的多光谱遥感影像变化检测精度较低的问题,提出一种基于SVM混合核的遥感图像变化检测。首先利用CVA算法构造差异影像,其次利用灰度共生矩阵提取差异影像的纹理特征与差异影像的灰度特征组成特征向量,接着利用差异影像的直方图选择置信度高的训练样本,并利用构造的SVM混合核进行训练得到分类超平面,最后利用SVM混合核函数对差异影像进行二分类得到最后的变化检测结果。实际遥感数据验证结果表明,所构造的SVM混合核函数用于多光谱遥感影像变化检测中是可行、有效的。     

An accurate traffic classification model based on support vector machines

Network traffic classification is a fundamental research topic on high‐performance network protocol design and network operation management. Compared with other state‐of‐the‐art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real‐time accurate SVM training model named SPP‐SVM is proposed. An SPP‐SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP‐SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP‐SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two‐ and multi‐class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP‐SVM's two‐ and multi‐class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.     

基于核函数Fisher鉴别的异常入侵检测

将核函数方法引入入侵检测研究中,提出了一种基于核函数Fisher鉴别的异常入侵检测算法,用于监控进程的非正常行为。首先分析了核函数Fisher鉴别分类算法应用于入侵检测的可能性,然后具体描述了核函数Fisher鉴别算法在异构数据集下的推广,提出了基于核函数Fisher鉴别的异常入侵检测模型。并以Sendmail系统调用序列数据集为例,详细讨论了该模型的工作过程。最后将实验仿真结果与其它方法进行了比较,结果表明,该方法的检测效果优于同类的其它方法。     

基于多特征融合的红外图像分类研究

针对传统红外图像目标分类方法准确率低的问题,提出了一种用结合多特征融合的粒子群优化(Particle Swarm Optimization, PSO)算法来优化支持向量机(Support Vector Machine, SVM)的方法。该方法采用方向梯度直方图(Histogram of Oriented Gradient, HOG)和局部二值模式(Local Binary Pattern, LBP)两类特征描述红外图像中目标的轮廓特征和局部纹理,从不同的方面展现红外图像的特点,在图像的特征表达上具有一定的互补性。在特征提取后对样本数据进行凸包算法计算,得到一些具有代表性的样本数据,从而提高分类计算效率;在分类模型训练时,采用PSO算法优化SVM,寻找SVM的最优惩罚因子和核参数,从而提高分类模型的准确率。实验结果表明,多特征融合的分类模型的准确率比单一特征的分类模型提高近10%,且经PSO优化的SVM最终模型的分类准确率高达99%。     

面向非平衡数据分类的概率过抽样过滤方法

利用非合作博弈理论为概率过抽样合成的少数类数据决定其最可能的类标签,将数据中的非本类合成数据进行过滤,减少概率过抽样合成数据过程中产生的重叠数据,得到更高质量的少数类数据进而改善数据倾斜状况。实验分别以CART和SVM分类器建立模型,将本文提出的面向非平衡数据分类的概率过抽样过滤方法RACOG+F与原始概率过抽样方法分别在8个KEEL非平衡数据集上进行对比。实验表明,本文提出的方法在评价指标F-measure、G-mean和AUC上获得了较好的分类性能。     

DATA PREPROCESSING AND RE KERNEL CLUSTERING FOR LETTER

Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification （KNN）, Radial Basis Function Neural Network （RBFNN）, and Support Vector Machine （SVM）. Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.