The effect of features reduction on different texture classifiers

The aim of this study was to investigate the performance of texture analysis in texture classification and tissue discrimination between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM) in order to support early diagnosis of MS. T2-weighted MR images of sixteen relapsing remitting MS (RRMS) patients and sixteen healthy subjects were selected. Based on the lesion size, sixteen regions of interests (ROIs) were chosen from MS patient MR images and healthy subject MR images for MS lesions, NAWM and NWM respectively. Texture features extracted from grey level co-occurrence matrix (GLCM) were selected based on greatest feature difference. For statistical analysis, raw data analysis (RDA), principal component analysis (PCA) and nonlinear discriminant analysis (NDA) were applied to the texture features. The k-nearest neighbor (k-NN) and artificial neural network (ANN) methods were used for texture classification. Fisher coefficient and classification accuracy were used to evaluate the performance of texture analysis. The results demonstrated that (1) classification was successful (>90.00%) between MS lesions and NAWM or NWM, less successful (88.89%) among the three tissue groups and worst (66.67%) between NAWM and NWM; (2) In statistical analysis, NDA outperforms RDA and PCA; (3) ANN classified more accurately than k-NN method between NAWM and NWM, and among the three texture types. This study demonstrated that MRI texture analysis can achieve high classification accuracy in tissue discrimination between MS lesions and NAWM or NWM, which is valuable in supporting early diagnosis of MS.

Correlations among Acoustic, Texture and Morphological Features for Breast Ultrasound CAD

Acoustic, texture, and morphological features of the breast in ultrasound imaging were extracted for computer-aided diagnosis. In addition, correlations among different categories of features were analyzed. Clinical data from 14 patients (7 malignant and 7 benign samples) were acquired. A custom-made setup was used for simultaneous data acquisition of B-mode ultrasound and limited-angle tomography images. Texture features were extracted from B-mode images, including five parameters derived from the gray-level concurrence matrix and five parameters derived from a nonseparable wavelet transform. Morphological features were also extracted from B-mode images, including the depth-to-width ratio and normalized radial gradient. Acoustic features were estimated using limited-angle tomography, including the sound velocity and attenuation coefficient. Generally, the correlation coefficients for features within the texture feature group were relatively high (0.48-0.79), whereas those between different feature categories were relatively low (0.17-0.40). This suggests that combining different sets of features would improve the computer-aided diagnosis of breast cancer.

Generally, an IDS (10) is responsible to find out the attack and normal behavior analyzing intrusion detection datasets. But sometimes the attacker intelligence causes fail to identify the attack. Because attacker having various methods to attack which cannot be every time identifiable by an IDS. This paper provides a divide and conquers feature reducing and feature selection algorithm to reduce the feature set from a large KDD 99 dataset. Then reduced feature sets are classified on a KDD dataset with the help of the Tanagra data mining tool. So, the proposed algorithm is selected important feature set and classified with maximized classification rate. In every data base have their records with large number of features. Among the feature sets some features may be ir- relevant and the rest of the feature may be important. So, before going to perform any operation on a large dataset, it is very useful to reduce the database into the important feature set. Now the task to identify the important feature sets is more or less difficult. But, many scientists already succeeded to develop their own technique to identify im- portant feature sets. Generally, in intrusion detection system scientists are used KDD dataset which having 41 features for every record with 37 different types of attacks. So, for reducing the feature set we have to find hidden relationship in the data set which may help us to find out the important features. In this paper divide and conquer algorithm is used to find the hidden relationship to select the important feature sets. The proposed algorithm is applied to KDD datasets for both feature selection and classification using the reduced feature sets with the help of Tanagra7 tool. Now-a-days rapid increased of network system needs more security for protecting the unauthorized access to the security of information sharing and access. As, most of the attackers have dynamic behavior, success to access the unauthorized information. To protect the attacker KDD database recorded with taking most of the dynamic behavior of the attacker in TCP dump format and then the datasets are analyzed to detect attack or normal behavior by an intrusion detection system. But an IDS may take long time to analyze the KDD data set, due to large records and 41 features. So, the proposed algorithm selects important feature sets and reducing the overhead of IDS for analyzing the whole KDD dataset. The rest of the paper is kept as, section II, providing some related work, section III, giving divide and conquer algorithm, section IV, providing experimental result, section V, concluding the paper.

Could linear model bridge the gap between low-level statistical features and aesthetic emotions of visual textures?

With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The process of texture analysis comprises the following important steps: texture classification, segmentation and synthesis. Texture is a significant property of images which is difficult to define even though the human eye may recognize it with ease. Texture classification has remained an intangible pattern recognition task despite numerous studies. The prime issue in any texture classification application is how to select the features and which features to consider for representing texture. Another major issue remains the type of metric to be used for comparing the feature vectors. The traditional way of texture classification is to convert the texture image into a vector representing the features using a set of filters. This is followed by classification with a few smoothening steps involved in between. This paper outlines a picture of the basic features of an image as texture, colour and shape which are to be extracted to form the feature vector. The concept of applying Machine learning for the purpose of classification is explored and implemented. The use of Soft Computing Technique—Artificial Neural Networks is illustrated for the purpose of classification. The texture features are extracted using the GLCM method and used as input and fed to the neural network. The algorithm used in training the neural networks is the traditional backpropagation algorithm. The results show which configuration of the multi-layer feedforward architecture is best suited according to our experimental set-up.

Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor.

This paper proposes to improve the classification accuracy of the leaf images by extracting texture and statistical features by utilizing the presence of striking features on the dorsal and ventral sides of the leaves, which on other types of objects may not be that prominent. The texture features have been extracted from dorsal, ventral and a combination of dorsal-ventral sides of leaf images using Gray level co-occurrence matrix. In addition to this, this work also uses certain general statistical features for discriminating them into various classes. The feature selection work has been performed separately for the dorsal, ventral and combined data sets (for both texture and statistical features) using the most common feature selection algorithms. After selecting the relevant features, the classification has been done using the classification algorithms: K-Nearest Neighbor, J48, Naive Bayes, Partial Least Square (PLS), Classification and Regression Tree (CART), Classification Tree(CT). The classification accuracy has been calculated and compared to find which side of the leaf image (dorsal or ventral) gives better results with which type of features(texture or statistical). This study reveals that the ventral leaf features can be another alternative in discriminating the leaf images into various classes.

Objectives: To identify a cervical cytology cell image as a representative of the normal or malignant cancerous cell. This work may be used to decide the stage of cervical cancer. Methods and Analysis: Images from the Hervlev university database have been used. Features of nucleus and cytoplasm of cervical cell images are extracted after preprocessing and segmentation. Size, shape and texture features are extracted. Appropriate and adequate features are selected by mining techniques. This feature set is fed to classifiers to decide nature of cells. Based on parameters like true positive rate, true negative rate and precision, a comparative analysis of classifiers is done. The best classifier that clearly discriminates the cells is determined for cervical cancer diagnosis. Findings: Dimensionality reduction of feature set can be done with data mining technique named as Correlation based feature subset selection. Reduced feature set can be used for classification by classifiers like Multilayer perceptron, Bayes classifiers and SVM classifier. Kappa Statistics for binary classifiers is 1 for Multilayer perceptron, 0.9968 for Binary SMO and Bayesnet, 0.9915 for NaiveBayes Classifiers. The same for seven group classifiers is 1 for LibSVM, 0.8535 for Multilayer perceptron, 0.8464 for attribute selected classifier and 0.7286 for Bagging classifier. True positive rate is 1 for Multilayer perceptron, 0.996 for Binary SMO and BayesNet, 0.975 for NaiveBayes classifier. Multilayer perceptron can be used as a classifier in both binary and seven class classifications. Improvement: Existing methods use more features for classification. Here even in the case of seven class classifier, only 11 features are used and desired result has been achieved.

This paper investigates the applicability of time and time-frequency features\nbased classifiers to distinguish internal faults and other transients -\nmagnetizing inrush, sympathetic inrush, external faults with current\ntransformer saturation, and overexcitation - for Indirect Symmetrical Phase\nAngle Regulating Transformers (ISPAR). Then the faulty transformer unit\n(series/exciting) of the ISPAR is located, or else the transient disturbance is\nidentified. An event detector detects variation in differential currents and\nregisters one-cycle of 3-phase post transient samples which are used to extract\nthe time and time-frequency features for training seven classifiers. Three\ndifferent sets of features - wavelet coefficients, time-domain features, and\ncombination of time and wavelet energy - obtained from exhaustive search using\nDecision Tree, random forest feature selection, and maximum Relevance Minimum\nRedundancy are used. The internal fault is detected with a balanced accuracy of\n99.9%, the faulty unit is localized with balanced accuracy of 98.7% and the\nno-fault transients are classified with balanced accuracy of 99.5%. The results\nshow potential for accurate internal fault detection and localization, and\ntransient identification. The proposed scheme can supervise the operation of\nexisting microprocessor-based differential relays resulting in higher stability\nand dependability. The ISPAR is modeled and the transients are simulated in\nPSCAD/EMTDC by varying several parameters.\n

A writer identification system for on-line whiteboard data

Texture analysis is considered fundamental and important in the fields of pattern recognition, computer vision and image processing. Analysis of the textures involves texture features extraction and selection, and plays an essential role in the classification and segmentation of textural features. In this study have compared two texture classification methods based on the Random Forest (RF) and Decision Tree (DT) classifiers by using a combination method between various extraction features, such as bi-orthogonal wavelet transform, gray level histogram and edge detection. Experiments were conducted on two different databases. The first texture database captured digital images for testing multi-class machining processes, and the second database was collected from the Brodatz album. The results have revealed that RF and DT have yielded higher classification precision.

Abstract. In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of correlation clustering lies in its ability to simultaneously perform feature reduction and clustering. This algorithm also allows selection of different sets of features for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. ORCLUS, a correlation clustering algorithm, is implemented and enhanced by making use of segmented principal component analysis (SPCA) instead of principal component analysis (PCA). Further, original implementation of ORCLUS makes use of eigenvectors corresponding to smallest eigenvalues whereas in this study eigenvectors corresponding to maximum eigenvalues are used, as traditionally done when PCA is used as feature reduction tool. Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.

Decomposition of heterogeneous classification problems

EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions