A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.     

基于支持向量机的电路故障诊断模型

提出了一种基于遗传编程和支持向量机的故障诊断模型。通过遗传编程对时域指标进行特征选择和提取，得到更能反映信号本质的特征信号，该特征信号可作为识别特征输入多类支持向量机，实现对模拟电路不同类型软故障的识别。实验结果表明，同传统时域指标相比，经过遗传选择和提取的特征对模拟电路的软故障具有更好的识别能力，进而提高了多类支持向量机的分类准确性。     

Automatic classification of computed tomography brain images using ANN,k-NN and SVM

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.     

Automatic RNA virus classification using the Entropy-ANFIS method

Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depends on manual controls. Moreover, detailed knowledge about RNA viruses is required. This study introduces the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS method), which can be used to automatically detect RNA virus images. This system consists of four stages: pre-processing, feature extraction, classification and testing the Entropy-ANFIS method with respect to the correct classification ratio. In the pre-processing stage, a center-edge changing method is used, in which the Euclidian distances are calculated from the center pixels to the edges of the imaged object. In this way, the distance vector is obtained. This calculation is repeated for each RNA virus image. In feature extraction, stage norm entropy, logarithmic energy and threshold entropy values are calculated to form the feature vector. The obtained feature vector is independent of the rotation and scale of the RNA virus image. In the classification stage, the feature vector is given as input to the ANFIS classifier, ANN classifier and FCM cluster. Finally, the test stage is performed to evaluate the correct classification ratio of the Entropy-ANFIS algorithm for the RNA virus images. The correct classification ratio has been determined as 95.12% using the proposed Entropy-ANFIS method.     

一种KCCA融合局部特征和全局特征的目标识别算法

提出一种新的KCCA特征融合算法。首先分别提取目标图像的局部特征SIFT和全局Pseudo-Zernike矩特征,并利用K-means算法对局部特征进行预处理;然后利用KCCA将两种特征提取相关特征进行融合,最后将融合特征送入SVM分类器。对遥感飞机图像库做了分类识别的仿真实验。相比于单一特征和CCA特征融合的识别策略,KCCA识别率得到明显提高,理论分析和实验结果证实了该算法具有良好的准确性与可靠性,能够有效提高图像分类识别系统的准确度。     

Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods – texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum – are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.     

基于不变矩特征及SVM的图像识别

该文提出了一种利用不变矩和支持向量机(SVM)对图像进行识别的方法。该方法提取图像的7个不变矩作为特征矢量,对得到的特征量应用支持向量机进行图像分类和识别。该文通过试验验证了此方法的有效性。     

基于自适应特征选择和SVM的图像分类的研究

提出多特征结合的图像分类方法,分别提取颜色特征和LBP纹理特征,同时提出Adaboost算法对特征进行选择,选择最能表示图像的特征,这样既降低了特征的维数,又提高了分类的精度。最后对基于SVM的多类图像分类方法进行了研究,提出在二类支持向量机的基础上构造多类分类器的方法,实验结果表明,提出的方法能够很好地用于图像分类。     

基于深度神经网络的Android恶意软件检测方法

Android 系统正日益面临着恶意软件的攻击威胁。针对支持向量机等传统机器学习方法难以有效进行大样本多分类的恶意软件检测,提出一种基于深度神经网络的Android恶意软件检测与家族分类方法。该方法在全面提取应用组件、Intent Filter、权限、数据流等特征基础上,进行有效的特征选择以降低维度,基于深度神经网络进行面向恶意软件的大样本多分类检测。实验结果表明,该方法能够进行有效检测和分类,良性、恶意二分类精度为 97.73%,家族多分类精度可达到 93.54%,比其他机器学习算法有更好的分类效果。     

基于DT-CWT和SVM的踏面旋转不变纹理提取算法

针对列车车轮踏面旋转纹理信息无法准确、有效提取的问题,提出一种基于Radon变换和双树复小波变换（DT-CWT）的列车车轮踏面特征提取方法。首先,对车轮踏面图像进行Radon变换;然后,对变换后的图像进行DT-CWT分解,使用分解后的各层低频子带系数和高频子带系数模的均值和标准方差构造特征向量,将其作为区分列车车轮踏面是否发生损伤的依据;最后,由支持向量机（SVM）进行分类决策。使用动车所采集的图像及人为加噪声后的图像进行分类实验,结果表明,本文使用的Radon和DT-CWT算法能有效地进行旋转不变纹理的提取,SVM分类正确率可以达到95%,可为列车车轮踏面状况检测提供更为准确便捷的方法支撑。     

An improved approach to steganalysis of JPEG images

Steganography secretly embeds additional information in digital products, the potential for covert dissemination of malicious software, mobile code, or information is great. To combat the threat posed by steganography, steganalysis aims at the exposure of the stealthy communication. In this paper, a new scheme is proposed for steganalysis of JPEG images, which, being the most common image format, is believed to be widely used for steganography purposes as there are many free or commercial tools for producing steganography using JPEG covers.First, a recently proposed Markov approach [27] is expanded to the inter-block of the discrete cosine transform (DCT) and to the discrete wavelet transform (DWT). The features on the joint distributions of the transform coefficients and the features on the polynomial fitting errors of the histogram of the DCT coefficients are also extracted. All features are called original ExPanded Features (EPF). Next, the EPF features are extracted from the calibrated version; these are called reference EPF features. The difference between the original and the reference EPF features is calculated, and then the original EPF features and the difference are merged to form the feature vector for classification.To handle the large number of developed features, the feature selection method of support vector machine recursive feature elimination (SVM-RFE) and a method of multi-class support vector machine recursive feature elimination (MSVM-RFE) are used to select features for binary classification and multi-class classification, respectively. Finally, support vector machines are applied to the selected features for detecting stego-images.Experimental results show that, in comparison to the Markov approach [27], this new scheme remarkably improves the detection performance on several JPEG-based steganographic systems, including JPHS, CryptoBola, F5, Steghide, and Model based steganography.     

基于小波变换和多类支持向量机的图像分类

提出了一种基于小波变换和多类支持向量机的图像分类新方法,该方法利用小波变换进行图像特征提取,利用多类支持向量机进行图像分类,并与基于图像底层特征的图像分类方法进行了实验比较。实验结果表明该方法具有较好的分类性能。     

采用圆周对齐边缘方向直方图的视频烟雾检测方法

视频烟雾检测具有响应速度快、非接触等优点,但由于烟雾形状、色彩、纹理千差万别,目前的算法很难取得令人满意的检测效果.为此,提出了一种鲁棒的特征提取方法,采用支持向量机(support vector machine,简称SVM)进行检测.首先,提取边缘方向直方图(edge orientation histogram,简称EOH).然后,采用圆周平移方式将EOH的最高柱变换到EOH的固定位置,消除了旋转变换的影响.为了进一步增强特征的鲁棒性,提取图像亮度和饱和度分量的Hu不变矩、均值、偏差、偏度和峰度特征.最后,将这些特征组成一个38维的特征矢量,采用SVM训练和识别烟雾.实验结果表明,这些特征具有很好的分类性能,能够在较大的训练库和测试库上达到98%和85%以上的检 测率.     

基于双树复数小波的视觉信息处理模型

本文基于双树复数小波变换的多尺度、多方向选择特性,对Jim Mutch提出的视觉信息处理模型进行改进。改进后算法大大提高了运算速度,同时获得的特征向量具有尺度、旋转和平移不变性。最后,利用支持向量基进行分类识别。Cal-tech101人脸数据库的实验结果表明,该方法能够取得较好的分类效果。     

Extraction of shift invariant wavelet features for classification of images with different sizes

An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.     

Automatic clinical image segmentation using pathological modeling,PCA and SVM

Due to the presence of complicated topological and residual features, the segmentation of medical imagery is a difficult problem. In this paper, an automated approach to clinical image segmentation is presented. The processing of these images in our approach is divided into learning and segmentation stages to facilitate the application of principal component analysis with a support vector machine (SVM) classifier. During the initial learning stage, representative images are chosen to represent typical input images. These images are segmented using a variational level set method driven by a modeled energy functional designed to delineate the pathological characteristics of the images. Then a window-based feature extraction is applied to these segmented images. Principal component analysis is applied to these extracted features and the results are used to train an SVM classifier. After training the SVM, any time a clinical image needs to be segmented, it is simply classified with the trained SVM. By the proposed method, we take the strengths of both machine learning and the variational level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. To test the proposed system, both chest (thoracic) computed tomography (CT) scans (2D and 3D) and dental X-rays are used. Promising results are demonstrated and analyzed. The proposed method can be used during pre-processing for automatic computer-aided diagnosis.     

A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS

This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.     

融合Hu矩与BoF-SURF支持向量机的手势识别

基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果, 但是计算复杂度高、实时性较差。为此, 提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征, 提高了实时性, 并引入Hu矩描述手势全局特征, 进一步提高识别率。实验结果表明, 算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。     

Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval

The pulse-coupled neural network (PCNN) has been widely used in image processing. The outputs of PCNN represent unique features of original stimulus and are invariant to translation, rotation, scaling and distortion, which is particularly suitable for feature extraction. In this paper, PCNN and intersecting cortical model (ICM), which is a simplified version of PCNN model, are applied to extract geometrical changes of rotation and scale invariant texture features, then an one-class support vector machine based classification method is employed to train and predict the features. The experimental results show that the pulse features outperform of the classic Gabor features in aspects of both feature extraction time and retrieval accuracy, and the proposed one-class support vector machine based retrieval system is more accurate and robust to geometrical changes than the traditional Euclidean distance based system.