Study and Review of Various Image Texture Classification Methods

Texture is one of the important characteristics used in identifying objects (or) regions of interest in an image. This can be identified by aerial or satellite photographs, biomedical images and other types of images [1]. In th e field of computer vision, texture classification is an important task. Texture classification is used in different pattern recognition application. It retains feature - liked appearance. This paper examines, analyzing the feature extraction towards texture and non - texture classification. In this paper we present texture classification and the feature extraction methods used in the research. Different extraction methods were introduced and used for texture classification problems.

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/persian-signature-verification-using-convolutional-neural-networks https://www.ijert.org/research/persian-signature-verification-using-convolutional-neural-networks-IJERTV1IS2001.pdf The objective of this paper is to recognize different textures in an image, particularly a satellite image where properties of the image are not distinctly identified. Texture classification involves determining texture category of an observed image. The present study on Image Processing & Texture Classification was undertaken with a view to develop a comparative study about the texture classification methods. The algorithms implemented herein classify the different parts of the image into distinct classes, each representing one property, which is different from the other parts of the image. The aim is to produce a classification map of input image where each uniform textured region is identified with its respective texture class. The classification is done on the basis of texture of the image, which remains same throughout a region, which has a consistent property. The classified areas can be assigned different colours, each representing one texture of the image. In order to accomplish this, prior knowledge of the classes to be recognized is needed, texture features extracted and then classical pattern classification techniques are used to do the classification. Examples where texture classification was applied as the appropriate texture processing method include the classification of regions in satellite images into categories of land use. Here we have implemented two methods namely-Cross Diagonal Texture Matrix (CDTM) and Grey-Level Co-occurrence Matrix (GLCM), which are based on properties of texture spectrum (TS) domain for the satellite images. In CDTM, the texture unit is split into two separable texture units, namely, Cross texture unit and Diagonal texture unit of four elements each. These four elements of each texture unit occur along the cross direction and diagonal direction. For each pixel, CDTM has been evaluated using various types of combinations of cross and diagonal texture units. GLCM, on the other hand, is a tabulation of occurrence of different combinations of pixel brightness values (grey levels) in an image. Basically, the GLCM expresses the spatial relationship between a gray-level in a pixel with the gray-level in the neighboring pixels. The study focuses on extraction of entropy, energy, inertia and correlation features using several window sizes, which are calculated, based on the GLCM. A maximum likelihood supervised classifier is used for classification. While applying the algorithms on the images, we characterize our processed image by its texture spectrum. In this paper we deal with extraction of micro texture unit of 7X7 window to represent the local texture unit information of a given pixel and its neighborhood. The result shows that increasing the window size showed no significant contribution in improving the classification accuracy. In addition, results also indicate that the window size of 7x7 pixels is the optimal window size for classification. The texture features of a GLCM and CDTM have been used for comparison in discriminating natural texture images in experiments based on minimum distance. Experimental results reveal that the features of the GLCM are superior to the ones given by CDTM method for texture classification.

International Journal of Research in Computer Science, 2012

This paper presents the comparison of Texture classification algorithms based on Gabor Wavelets. The focus of this paper is on feature extraction scheme for texture classification. The texture feature for an image can be classified using texture descriptors. In this paper we have used Homogeneous texture descriptor that uses Gabor Wavelets concept. For texture classification, we have used online texture database that is Brodatz's database and three advanced well known classifiers: Support Vector Machine, K-nearest neighbor method and decision tree induction method. The results shows that classification using Support vector machines gives better results as compare to the other classifiers. It can accurately discriminate between a testing image data and training data.

2011

Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern

In this paper, a novel classification system for colour texture images based on Gray Level Cooccurrence Matrix (GLCM) is presented.

International Journal of Advanced Robotic Systems, 2014

This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and exper...

Expert Systems With Applications, 2009

Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.

Computer Science & Information Technology ( CS & IT ), 2012

Texture is the term used to characterize the surface of a given object or phenomenon and is an important feature used in image processing and pattern recognition. Our aim is to compare various Texture analyzing methods and compare the results based on time complexity and accuracy of classification. The project describes texture classification using Wavelet Transform and Co occurrence Matrix. Comparison of features of a sample texture with database of different textures is performed. In wavelet transform we use the Haar, Symlets and Daubechies wavelets. We find that, thee 'Haar' wavelet proves to be the most efficient method in terms of performance assessment parameters mentioned above. Comparison of Haar wavelet and Cooccurrence matrix method of classification also goes in the favor of Haar. Though the time requirement is high in the later method, it gives excellent results for classification accuracy except if the image is rotated.

2008

Texture classification is one of the most important clues of visual processing applications .In this paper, we present a comparison between the most two popular supervised texture classification methods based on the feed forward Artificial Neural Network (ANN) and the multi-class Support Vector Machine (SVM). Five of the most common used features extraction approaches were chosen in order to extract input vectors of different sizes for both classifiers. These approaches are namely gray level histogram, edge detection, and co-occurrence matrices, besides Gabor and Biorthogonal wavelet transformations. Experiments are conducted on two different datasets the first one is engineering surface textures produced by different machining processes, and the second was taken from Brodatz (1966) textures album. The classification accuracy rate is calculated for ANN and SVM in order to measure the efficiency of each technique based on the several features extraction methods. The results show that SVM with its linear and polynomial kernels is higher in classification accuracy and faster in training time.

Texture is an important characteristic for image analysis. This paper contains study and review of most major image texture classification approaches using different wavelet domain feature extraction algorithms, which have been proposed in the recent literature for image-texture classification, and perform a comparative study. The objective of study is to offer a comparison study to help other researchers to find technique or combination of techniques to reduce complexity, while increasing the accuracy at the same time.

2014 World Symposium on Computer Applications & Research (WSCAR), 2014

Texture is an important feature for image anal y sis in which each pixel is classified based on its neighborhood. It is used for surface characterization in man y applications, such as medical imaging, remote sensing and qualit y control. The purpose of this paper is to investigate the performance for the newl y purposed Local directional pattern (LDP) and compared to the popular Gra y level co-occurrence matrix (GLCM). In this paper, texture classification power of two feature methods, Gra y Level Co-occurrence Matrix (GLCM) and Local Directional Pattern(LDP) are compared. Experiments are conducted on 25 Texture t y pes selected from Brodatz album. Classification are carried out using 4 different classifiers (Naive-ba y es(NB), Multila y er Perceptron(MLP), Support Vector Machine (SVM), k-nearest Neighbor Algorithm(k-NN» in different conditions. In this stud y it is established that the LDP has the best accurac y at 97% using Multila y er Perceptron and 96% using SVM, compared to GLCM. In the literature Local Directional Pattern (LDP) has mainl y been used to extract features in biometrics applications. In this paper LDP is used to characterize general purpose textures. It is shown that outperforms the ver y popular Gra y Level Co-occurrence Matrix and Haralick features.

4th International Conference on Advances in Medical, Signal and Information Processing, 2008

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture methods and classifying the patterns using a naïve Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based -Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) -were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as Gray level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively.

Journal of Computer Science, 2007

The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That's why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns.

2014

Nearest neighbor classifier is used for classification. Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. The nearest-neighbor classifier is the simplest of all algorithms for predicting the class of a test example. The training phase is trivial: simply store every training example, with its label. By coupling the LBP and genetic algorithm for texture analyses and nearest neighbor for texture classification, a more optimized Texture is an important visual clue for vision system. However there is no clear understanding of the nature of texture and texture analysis is still an unsolved problem. As an emerging problem solving method, genetic algorithm has been successfully adapted to various complex tasks in classification and image analysis. texture classification method is presented.

Sensor Review, 2012

Purpose-The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach-The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings-The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications-Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value-This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.