基于感知器的SVM自学习模型

提出了一种基于感知器的SVM分类模型（PSVM）。该模型在对分类器的训练中,引入感知器分类思想,其先利用SVM的核函数进行核计算,判断其分类性能,分类正确则不作任何修改,反之则转化成感知器分类问题。实验结果表明该模型不但能提高SVM的分类性能,而且还可以降低SVM分类性能对核函数及参数选择的依赖。     

基于机器视觉的随机纹理瓷砖的分选系统

针对日益加快的瓷砖生产速度与缓慢的人工分选速度之间不协调导致的瓷砖出产效率低下的问题,提出了以机器视觉软件HALCON 11.0为软件开发平台的结合瓷砖颜色、纹理特征提取的算法,以及针对多分类问题的改进多层感知器神经网络算法(MLPNN).首先对拍摄到的瓷砖图像进行去噪预处理,在HSI颜色空间中提取瓷砖的色调(Hue)特征并计算反映瓷砖的纹理特征的灰度共生矩阵(GLCM)和灰度幅值分布特征,再将得到的特征作为多层感知器的神经网络输入层神经元,然后设计以softmax为激活函数的多层感知器神经网络来进行模式匹配,并与BP神经网络模式匹配方法进行对比,最终搭建出具有简单人机交互界面的随机纹理瓷砖的分选实验样机.实验结果表明:本系统对实验的各类随机纹理瓷砖的分选准确率都在90%以上,具有较高的分选准确率,能应用于瓷砖生产实践.     

基于神经网络的三维模型视觉特征分析

首先从形状、颜色、纹理材质三个主要视觉特性入手,阐述模型的特征描述符,设计三元组视觉特征向量用于神经网络进行模型分类。具体基于感知器神经网络、Hopfield神经网络分别实现了对三维物体的分类。实验表明,基于神经网络的分类器能对基于视觉特征描述的三维物体进行有效识别。     

基于环形马尔可夫模型的纹理图像分类

提出了一种基于环形马尔可夫模型的纹理图像分类方法。利用环形邻域构建马尔可夫模型,在模型参数求解过程中,为了避免矩阵奇异,设计了模型参数分步求解算法。基于环形马尔可夫模型提取纹理特征,然后基于动态邻域Tabu搜索算法进行特征选择,得到最优的纹理特征子集,输入到最大似然法分类器中实现图像的分类。实验结果证明,与传统的栅格马尔可夫模型相比,环形马尔可夫模型能够更好地描述纹理图像像元之间的空间相关性信息,大大提高了纹理图像分类精度。     

基于贝叶斯网络模型的纹理分析及分类

用贝叶斯网络对纹理图像建模,并基于此模型给出了一种纹理分类的方法.把纹理图像在一个窗口内各个像素的灰度值看作贝叶斯网络的一次实现,通过训练得到各类纹理所对应贝叶斯网络的结构和参数,用纹理图像像素点在网络中的条件概率分布作为特征进行纹理分类.实验结果证明了该方法的有效性.     

瓷砖图像的纹理特征分类研究

在已有的瓷砖图像分类系统中,仅靠颜色特征和简单的纹理边缘信息只能对无花纹的单色砖或简单花纹的瓷砖进行有效分类,对复杂图案的瓷砖存在识别率低的问题。针对此种情况,结合瓷砖图像的灰度共生矩阵和统计几何特征,将这些特征输入支持向量机进行特征分层分类。采用基于径向基核函数和[K]交叉验证法所得到的最优参数构造支持向量机,解决瓷砖纹理特征具有非线性的分类问题。用瓷砖生产线上采集的大量图像进行实验表明,该方法准确率高,分类效果好。     

基于空间模糊纹理光谱的多光谱遥感图像分类方法

为了充分利用各波段的纹理信息，针对遥感图像不同波段之间具有较大相关性的特点，提出了一种用空间模糊纹理光谱描述多光谱遥感图像纹理特征的方法。根据纹理特征具有多尺度的特性，对原始图像进行二次模糊纹理滤波，一次滤波采用平面三角隶属度函数，二次滤波采用空阃距离代替平面距离形成滤波隶属度函数，其模糊滤波图像的隶属度分布称之为空间模糊纹理光谱。用FasART神经网络分类验证，实验结果表明，该方法具有较高的分类精度，尤其对纹理特征较为复杂的区域的分类效果更为明显。     

基于径向基函数(RBF)映射理论的遥感影像分类模型研究

与传统统计方法的分类器相比较,人工神经网络（ＡＮＮ）方法应用于遥感影像分类,不需预先假设样本空间的参数化统计分布,具有复杂的映射能力。大多数ＡＮＮ分类器采用误差反向传播（ＢＰ）学习算法的多层感知器模型（ＢＰＮＮ）,其主要缺陷是学习速度缓慢、容易陷入局部极小而导致难以收敛等。基于径向基函数（ＲＢＦ）映射理论的神经网络模型融合了参数化统计分布模型和非参经线性感知器映射模型的优点,在实现快速学习的同时,     

基于多特征融合的恶意代码分类算法

针对多数恶意代码分类研究都基于家族分类和恶意、良性代码分类,而种类分类比较少的问题,提出了多特征融合的恶意代码分类算法。采用纹理图和反汇编文件提取3组特征进行融合分类研究,首先使用源文件和反汇编文件提取灰度共生矩阵特征,由n-gram算法提取操作码序列;然后采用改进型信息增益（IG）算法提取操作码特征,其次将多组特征进行标准化处理后以随机森林（RF）为分类器进行学习;最后实现了基于多特征融合的随机森林分类器。通过对九类恶意代码进行学习和测试,所提算法取得了85%的准确度,相比单一特征下的随机森林、多特征下的多层感知器和Logistic回归算法分类器,准确率更高。     

基于纹理分析的磁共振图象区域分割

本文介绍了一种用于医学磁共振脑部图象中的主要解剖结构区域分割的纹理分析方法. 利用纹理的二阶统计参数和局部分形维数,组成特征空间后,对象元进行两步分类:首先利用 K平均方法进行二值决策分类,然后采用概率松弛方法获得象元对各类的隶属概率.     

Learning texture discrimination masks

A neural network texture classification method is proposed in this paper. The approach is introduced as a generalization of the multichannel filtering method. Instead of using a general filter bank, a neural network is trained to find a minimal set of specific filters, so that both the feature extraction and classification tasks are performed by the same unified network. The authors compute the error rates for different network parameters, and show the convergence speed of training and node pruning algorithms. The proposed method is demonstrated in several texture classification experiments. It is successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background. Compared with the traditional multichannel filtering method, the neural network approach allows one to perform the same texture classification or segmentation task more efficiently. Extensions of the method, as well as its limitations, are discussed in the paper     

基于贴近度等级的链路质量评估方法

无线传感器网络中,链路具有波动性,为提高数据传输的准确率,可以通过链路质量评估避开差的链路。在目前的链路质量评估研究中,针对链路等级划分存在主观性和无统一性的问题,利用熵值法计算评估参数权重,消除主观因素在参数权重计算中的干扰。由于链路质量受多特征属性影响,采用贴近度分析法综合各种特征属性划分链路质量等级。在此基础上,提出一种基于贴近度等级的链路质量评估方法,采用类间离散度二叉决策树进行链路分类,建立了链路质量4级二叉树的支持向量机SVM评估模型。同时提出一种混合优化算法对核函数的参数寻优。实验结果表明,改进的参数寻优方法有效提高了模型评估的准确性,训练时间短;在多网络环境下,与基于LQI的链路质量评估模型和BP神经网络评估模型相比,该模型以较少的探测包更为准确地评估出链路质量,避免因发送大量探测包带来的能量开销,降低了能耗,具有很好的环境适应能力。     

Median radial basis function neural network

Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approach uses classical estimation in the learning stage and it is based on the learning vector quantization algorithm and its second-order statistics extension. After the presentation of this approach, we introduce the median radial basis function (MRBF) algorithm based on robust estimation of the hidden unit parameters. The proposed algorithm employs the marginal median for kernel location estimation and the median of the absolute deviations for the scale parameter estimation. A histogram-based fast implementation is provided for the MRBF algorithm. The theoretical performance of the two training algorithms is comparatively evaluated when estimating the network weights. The network is applied in pattern classification problems and in optical flow segmentation.     

Wood defects classification using laws texture energy measures and supervised learning approach

Machine vision based inspection systems are in great focus nowadays for quality control applications. The proposed work presents a novel approach for classification of wood knot defects for an automated inspection. The proposed technique utilizes gray level co-occurrence matrix and laws texture energy measures as texture feature extractors and feed-forward back-propagation neural network as classifier. The proposed work involves the comparison of gray level co-occurrence matrix based features with laws texture energy measures based features. Firstly it takes contrast, correlation, energy and homogeneity as input parameters to a feed-forward back propagation neural network to predict wood defects and then it take energy calculated from laws texture energy measures based energy maps as input feature to a feed-forward back propagation neural network. Mean Square Error (MSE) for training data is found to be 0.0718 and 90.5% overall average classification accuracy is achieved when laws texture energy measures based features are used as input to the neural network as compared to gray level co-occurrence matrix based input features where MSE for training data is found to be 0.10728 and 84.3% overall average classification accuracy is achieved. The proposed technique shows promising results to classify wood defects using a feed forward back-propagation neural network.     

基于线性回归的无线传感器网络链路质量估计方法研究

针对无线传感器网络链路质量估计模型中回归算法复杂度高、缺少统一分类标准和公开数据集等问题,提出了一种基于EWMA和线性回归的链路质量估计方法ELR-LQE。以物理层获取的RSSI、LQI和SNR,以及包接收率PRR作为度量参数,分别在多种实验环境中采用不同的发射功率、竞争条件和部署方式采集数据,建立了链路质量估计数据集。通过最小值填充和EWMA对数据进行预处理,明显提高了回归模型的输入特征与链路质量的相关性。与现有方法相比,提出方法易于和网络层协议适配,并且复杂度较低,适合在资源有限的无线传感器网络节点中实现。实验结果显示,ELR-LQE具有较高的精度,在多种实验条件下平均的ME为4.6×10-2,R2为0.99。     

基于粗糙集与BP神经网络的多因素预测模型

运用粗糙集方法和信息熵概念，在不改变训练样本分类质量的条件下，按照输入影响因素相对于输出的重要度的大小，对输入参数集进行约简，确定神经网络输入层变量和神经元个数。通过对典型样本的学习，建立粗糙集BP神经网络多因素预测模型，将其用于导弹系统研制费用预测。结果表明，该方法减少了网络的训练时间，改善了学习效率，具有较高的预测精度，是可行的、有效的。     

基于支持向量机的枪弹外观缺陷识别与分类

为解决枪弹外观缺陷自动分类问题,提出了一种基于支持向量机的枪弹外观缺陷自动识别与分类模型。首先针对枪弹表面缺陷的图像特点,从几何、灰度、纹理三方面进行了特征提取,在此基础上建立了基于支持向量机的枪弹外观缺陷分类模型,并对特征参数进行了优选;研究了支持向量机中惩罚系数和核函数参数对分类器性能的影响;通过实验与基于BP神经网络的枪弹外观缺陷分类器进行了比较,结果表明,在小样本下,基于支持向量机的枪弹外观缺陷分类器性能更好。     

Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.