A Brief Review on Texture Analysis Methods

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A Brief Review on Texture Analysis Methods Kuldeep Chaurasia1, P. K. Garg2 Geomatics Engineering Group, Civil Engineering Department, IIT, Roorkee, India kchaurasia.nitb@gmail.com; pkgiitr@gmail.com Abstract It is very difficult to identify an object just considering its color and shape. Texture is one of the important features which helps in identifying the objects that appear similar on the image but in reality they are different objects on the ground. It is commonly accepted that texture analysis plays a very significant role in object classification and outlining the important regions of a given gray level image. Image analysis process requires the knowledge of the characteristics of the pixels in an image. In this paper, five texture analysis methods namely; Structural, Statistical, Model based, Transform based and Soft computing have been described. Moreover, benefits and shortcoming of the different methods are also discussed.

Keywords Texture Analysis; Gray Level Co-Occurrence Matrix; Markov Random Field; Wavelet Transform; Fractals; Feature Extraction

Introduction Texture is an important visual property that is used to characterize the natural and artificial images which make texture a very useful feature for image analysis.

Texture, the term used to characterize the surface of a given object or phenomenon, undoubtedly one of the main features used in image processing and pattern recognition (Wechsler, 1979). Texture can be seen in images ranging from multispectral to microscopic images. The main requirement of image analysis process is the knowledge of the characteristics of the pixels in an image. If the analysis procedure is performed on the remote sensing images, it needs some more attention for the purpose of drawing definite conclusion. It requires the knowledge of the some additional parameters which can provide the information about the spatial relationship of a pixel with other pixels.

It is commonly accepted that texture analysis plays a fundamental role in classifying objects and outlining the significant regions of a given gray level image (Haralick, 1979). If the structural property of the texture is taken into consideration then the term texture generally refers to repetition of basic texture elements, called texels. The placement of texels may be random, periodic or quasi-periodic. Natural textures

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are usually random, whereas artificial textures are often periodic or quasi-periodic. Texture analysis process involves four important issues: feature extraction, texture segmentation, texture classification and shape from texture. The main purpose of texture analysis methods is to characterize image texture in terms of the features extracted from it.

The process of identifying an object in an image based on its texture has gained much popularity in last few years as texture based analysis found applications in many fields including pattern recognition, content based image retrieval and remote sensing etc.

Tuceryan (1994) proposed a moment based texture segmentation methodwho used moment as the texture feature to segment the image. A computational approach using channel filter such as Gabor functions for visible texture analysis was proposed by Bovik et al. (1990) who found the proposed approach feasible for both natural as well as artificial texture.

Drimbarean and Whelan (2001) suggested a method for image texture classification by incorporating the color information which enhanced the performance of the texture analysis process. Morgenthaler et al. (1982) presented a texture analysis of three dimensional data sets such as terrain elevation. Hammouche et al. (2006) proposed a clustering approach based on multidimensional texture analysis which adopted statistical method such as gray level co-occurrence matrix. Surveys on various texture analysis methods can be found in Rao, 1990, Tan, 1993, Gool et al. 1985.

Comparative analysis of texture methods have been provided in Nailon et al., 1996, Khazenie and Richardson, Singh and Singh, 2002.

The purpose of this review paper is to summarise the five texture analysis methods (i.e., Structural, Statistical, Model based, Transform based and Soft computing) popularly used in remote sensing data analysis. The Benefits and shortcoming of these methods are also discussed.

Texture Analysis Methods and Their Applications Applications of the texture analysis methods can be found in various fields including Document

Studies in Surveying and Mapping Science (SSMS) Volume 1 Issue 2, June 2013

processing (Moreno et al. (2005), Journet et al., (2008)), Medical image processing and analysis (Nielsen, (1997), Tahir et al. (2004)), Inspection and evaluation of food quality (Zheng et al., 2006), Content based image retrieval (Arebey et al., 2011), Landuse classification (Zhan et al. (2011), Urban area extraction (Lorette et al.(1999)), Vegetation mapping (Zhang and Xie, 2012), Tree species classification (Heinzel and Koch (2012)) etc. Texture analysis methods can be categorized into five classes including structural, statistical, model based, transform based and soft computing.

Structural Methods Structural approach of texture analysis represents texture by well defined primitives and hierarchy of spatial arrangement of those primitives. A primitive is a connected set of resolution cells characterized with a list of attributes. The simplest primitive is the pixel with its gray tone attribute. To describe the texture, one must define primitives and their placement rules.

Structural method alone is not capable of capturing or generating randomness so that it is used in combination with statistical methods (Huang et. al, 1980).

Model Based Methods Model based methods describe texture in an image using sophisticated mathematical models and model parameters from texture are extracted to perform analysis.

Auto regressiv e Model

Set of weights

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1) Auto Regressive Model This model considers a local interaction between image pixels in which pixel gray-level value is the weighted sum of the gray-level values of the neighbouring pixels. The auto regressive parameters are composed of set of weights which are used to establish the relations between groups of neighbouring pixels (Castellano et al., 2004).

2) Random Field Model Many model-based methods (including Gaussian Markov random fields (Cross and Jain, 1983), Gibbs random fields (Derin and Elliott, 1987) are introduced to model texture, in which, a texture image is modeled as a probability model or as a linear combination of a set of basic functions. The coefficients of these models are used to characterize texture images and often transformed into different forms invariant to translation, rotation and scale (Zhang and Tan, 2001). The key problem of these methods is how to estimate the coefficients of these models and how to choose the correct model suitable for the selected texture. Markov Random Field Model (MRF) is one of the well known and widely used model which gives the local contextual information in an image. A Markov random model is a probabilistic process in which all interaction is local. MRFs are multidimensional generalizations of Markov chains defined in terms of conditional probabilities associated with spatial neighbourhoods (Hassner and Sklansky, 1980).

Yousefi and Kehtarnavaz (2011) have proposed a new stochastic model based on Markov random field for texture modelling.

3) Fractals

Input Image

Markov Random Field/ Gibbs Random

Fractal s

Set of coeffi cients

Texture Analysi s

Fractal dimension

FIG 1. TEXTURE ANALYSIS USING MODEL BASED METHODS

The most popular model based approaches includes Autoregressive model, Markov random field model, Gibbs random field model and Fractals as summarised in (FIG.1).

Fractals are very useful and have become popular in modelling the properties like roughness and selfsimilarity at different scale. Texture is characterized with the provision of a very important parameter known as fractal dimension which correlates to roughness of the surface. Conc and Nunes (2001) who proposed a method for multi-band image analysis using local fractal dimension, have utilized fractal dimension to characterize the texturescaling behaviour. According to Chan (1992), there are two methods to derive fractal dimension, first of which considers the estimation of fractal dimension on the basis of average intensity difference of pixels within the region of interest, while the other utilizes the Fourier power spectrum

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of the image data to determine fractal dimension.

the repeatability of the texture as shown in (FIG. 3).

Statistical Methods Statistical methods represent texture indirectly by non deterministic properties that govern distribution and relationship between gray levels of an image. On the statistical level, it is regarded that the texture is defined by a set of statistics extracted from a large ensemble of local picture properties. Even simple statistics such as the gray level first-order statistics can be used to classifya limited class of textures transform methods (Tomita et al., 1992). Some of the commonly used statistical methods including Auto-correlation function, Gray level co-occurrence matrix and Histogram are discussed below and summarized in (FIG. 2).

Autocorr elation Function

FIG. 3 AUTO CORRELATION FUNCTION OF 1-D SIGNAL

It is used to measure the amount of regularity in addition to fineness/coarseness of texture present in the image. Autocorrelation function of an image X(i,j) can be defined as follows: ��(��,��)��(��+��,��+�� )

đ?‘€đ?‘€ đ?œŒđ?œŒ(i,j)=∑đ?‘€đ?‘€ đ?‘˘đ?‘˘=0 ∑đ?‘Łđ?‘Ł=0 ∑đ?‘ đ?‘

đ?‘ đ?‘ 2 đ?‘˘đ?‘˘ =0 ∑đ?‘Łđ?‘Ł=0 đ?‘‹đ?‘‹ (đ?‘–đ?‘–,đ?‘—đ?‘— )

(1)

where i, j are the positional differences in the u, v direction, respectively.

The autocorrelation function for coarse texture drops off very slowly but it drops off very rapidly for fine texture. For regular texture autocorrelation function exhibits peaks and valleys.

Autocorrelatio n coefficients

2) Histogram

Input image

Histogra m

Gray level cooccurren ce matrix (GLCM

)

First order statistics such as mean, variance, skewness, kurtosis etc. are obtained

Textur e analysi

Second order statistics such as Entropy, Energy, Contrast, Homogeneity etc. are obtained

FIG. 2 TEXTURE ANALYSIS USING STATISTICAL METHODS

1) Autocorrelation Function Spatial size of tonal primitives of an image is related to the texture. Larger and small size tonal primitives are indicative of coarser and fine textures, repectively. Size of the tonal primitives is best described by a feature known as autocorrelation function. The autocorrelation function shows both local intensity variations and

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The statistics present in a image can be described from the histogram of gray levels in a very concise and simple way. Single pixel used in calculation of gray level histogram, indicates that the histogram consists of first order statistical information about the image. The shape of histogram plays a very important role in identifying the image characteristics. For example, a low-contrast image has the narrowly distributed shape of the histogram. If the image contains the object with a very narrow intensity range on a background of deffering intensity, then bimodal shape of the histogram image is obtained. Various parameters can be obtained from histogram of the image including mean, variance, skewness and kurtosis to characterize the texture (Srinivasan and Shobha, 2008).

3) Gray Level Co-occurrence Matrix It is one of the well known and widely used second order statistics for texture features. Gray Level Cooccurrence Matrix (GLCM) is also known as Gray Tone Spatial Dependency matrix. The first and second order statistics (co-occurrence matrices) are by far the most used statistical methods for texture discrimination. The first order statistics uses original image values such as standard deviation but fails to consider the spatial relationship of neighbouring pixels. The second order statistics

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takes care of spatial relationship between groups of two pixels in original image. Some of the important texture features that can be obtained from GLCM are contrast, energy, homogeneity and entropy (Beliakov et al., 2008).

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Some advantages and disadvantages of the various texture analysis methods suggested by different researchers (Rosenfeld and Troy ,1970, Chan, 1992, Dubuisson and Dubes, 1994), have been summarized in (TABLE 1).

TABLE 1. ADVANTAGES AND DISADVANTAGES OF DIFFERENT TEXTURE ANALYSIS METHODS

S.No.

1

Method type

Statistical

Transform based

2

3

Model based

Texture Analysis method

Advantages

Disadvantages

Remarks

Autocorrelation Function

shows both local intensity variations and also the repeatability of the texture

Not a very good discriminator of isotropy in natural textures.

Not a satisfactory measure of coarseness.

Considers first order statistics, Easy to implement

Cannot capture spatial relation

Used to classify images with single texture such as wood, water, grass etc.

Gray-level co-occurrence matrix

Considers second order statistics and less susceptible to variation in first order statistics or such as change in brightness of the image

not particular effective for characterization of textures which are of low contrast and random in nature.

Used to classify images with mixed texture as present in satellite image

Fourier

Suitable for highly periodic textures such as those arise with many textiles

strong edges and image boundary effects can prevent accurate texture analysis

Wavelet

Suitable for high periodic textures, multi scale analysis is possible, provides better noise immunity. Low computational cost

texture analysis is affected in presence of strong edges, problem of the choice of right wavelet

Markov Random field model

Directionality, coarseness, gray level distribution, and sharpness can all be controlled by choice of the parameters

Regular textures are not modeled very well, Non geometric texture primitives, do not provide accurate models for textured images of many real surfaces

Histogram

Fractals

Insensitive to image scaling, Best describes the objects having high degree of irregularity

Isotropic in nature, not good for segmentation of all types texture

Auto regressive model

work well for vertical wiggly texture

poor performance with diagonal wiggly texture

Artificial neural network

4

Soft computing

Fuzzy logic

Genetic algorithms

used to recognize patterns, forecast future events and classify data with more accuracy Less computationally expensive, Properties can be described by identifying different stochastic relationships, greater classification accuracy than calculated for statistical methods Noisy and irrelevant features taken for classification are better refined

Over fitting caused by too many attributes, computationally expensive

Used for natural textures such as grass, sand and trees etc.

Used to analyse natural textures such as grass, sand and trees and manmade texture such as brick , wall etc.

Widely used with natural textures, allows us to consider neighbour in all direction, Good in describing many natural textures as well as many human tissues in medical images such as the ultrasonic image.

Used for texture synthesis and texture segmentation applications too Good classifier for natural objects, applicable to satellite images too

Output results depend on the prior knowledge

Good classifier for both natural and artificial texture

Training/output data representation is complex, not the most efficient method to find some optima

Good classifier for natural as well as artificial texture

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Transform Based Methods Transform based approach of texture analysis represents an image on space whose coordinate system has an interpretation that is closely related to the characteristics of the texture (such as size or frequency). There are many transform based methods such as Fourier (Czarnecka and Gillott, 1977), Gabor (Galasso and Lasenby, 2009) and Wavelet (Popovic, 1999). The main advantage of the transform based methods is the capability to operate in frequency domain in addition to the spatial domain.

Fourier Transf orm

Set of values in power spectrum

Texture Analysis

Input Image

Wavele t Transf orm

Mean, Variance of energy of subbands

of structures present on the image. Another useful benefit of the Fourier transform is that Fourier domain features are less sensitive to noise than spatial domain features. The shortcoming of the Fourier method is to provide poor spatial resolution which is later overcome by windowed Fourier transform at the cost of increased computational complexity.

2) Wavelet Transform Based Wavelet transform based method is one of the very popular approach in which image is decomposed in multiple scale and used for texture analysis.

Wavelets are mathematical functions that cut up data into different frequency components and then study each component with a resolution matched to its scale (Graps, 1995). The discrete wavelet transform (Young, 1993) decomposes a 2D signal into four components: LL, LH, HL and HH where HH, LH and HL are the high frequency components but LL is a low frequency component.

High frequency components are the representative of high frequency details while low frequency component represents low frequency details. The decomposition of an image can be best described by following (FIG. 5).

Input Image

Discrete Wavelet Transform

LL

LH

HL

HH

FIG. 4 TEXTURE ANALYSIS USING TRANSFORMED BASED METHODS

1) Fourier Transform Based Fourier transform based methods have been widely used for texture analysis because of its suitability to describe the periodic function such as feature image which usually consists of quasi-repetitive pattern. Fourier power spectrum values resulting from the application of the Fourier transform capture the dominant orientation of pattern in the image and their distribution in frequency domain closely related with the coarseness of texture. Thus, both the features orientation and coarseness of texture play a very crucial role in texture analysis (Abdesselam, 2009). The useful characteristics of the Fourier transform is the highlighting of the dominant spatial frequency as well as orientation

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FIG. 5. DISCRETE WAVELET TRANSFORM

Discrete wavelet transform of an image involves the decomposition of the input image using high pass and low pass filter. The decomposed image is then sampled at a half rate (1/2 down sampling) of the previous frequency and output is again fed to the high pass and low pass filters. Four components are obtained from sampling the output of the previous step. By repeating this procedure, it is possible to obtain higher order wavelet transforms. First level decomposition of the input image using wavelet transform has been summarized in (FIG. 6).

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h(n)

h(m)

2

LL

2 g(n)

2

HL

h(n)

2

LH

g(n)

2

H

Input Imag

g(m)

2

2

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1) Artificial Neural Network Neural networks are computer algorithms that follow the same way to process information as processed in nervous system. An artificial neuron is a computational model inspired in the natural neurons. One type of network regards the nodes as ‘artificial neurons called artificial neural networks (ANNs). Neural network and artificial intelligence methods are differentiated by their ability to learn (Chandankhede, 2011).

Represents the down sampling

FIG. 6 1ST LEVEL DECOMPOSITION OF 2-D SIGNAL

Soft Computing Based Methods Uncertainty, tolerant of imprecision and partial truth makes soft computing different from conventional (hard) computing. In effect, the role model for soft computing is human mind. Soft computing techniques exploit the uncertainty, partial truth and tolerant of imprecision to achieve flexibility, robustness and less expensive solution. The classifiers which are based on the soft computing technique are distribution independent and consider the inherent natural variation. This behavior help the classifier to better classify natural objects, and achieve greater classification accuracy than statistical methods. Soft computing may therefore can be employed to overcome the imperfect data problem in Geographical Information System (GIS). Shirkhodaie et al. (2005) have discussed different methods to detect silent terrain features based on texture analysis. Most popular soft computing methods include artificial neural network, fuzzy logic and genetic algorithms as shown in (FIG. 7).

Artificial neural network

Input image

Texture features

Fuzzy logic

FIG. 8 STRUCTURE OF ARTIFICIAL NEURAL NETWORK (Ahemd et al. (2011))

ANN is composed of three layers namely input, hidden and output as shown in FIG. 8. Raghu et al.

(1994) adopted two stage neural network approach for texture classification which is one of the important issue in texture analysis. Characteristics of both SOM and MLP have been combined together to be utilized in two stage neural network.

The objective of the Self Organising Map (SOM) is to transform the input signal pattern of arbitrary dimension into one or two dimensional discrete map. SOM is set up by placing neurons at the nodes of a one or two dimensional lattice which forms the feature space. Multi Layer perceptron (MLP) is a feed forward neural network with one or more layers between input and output layer.

Feature obtained from SOM are used as input to MLP for training and classification.

2) Fuzzy Logic

Texture classification

Genetic algorithms FIG. 7 TEXTURE CLASSIFICATION USING SOFT COMPUTING TECHNIQUES

Zadeh proposed a Fuzzy sets theory which deals with the imprecise information in 1965.

Membership function is used to represent imprecise information. Comparison between an observation which can be fuzzy or crisp and imprecise information is known as fuzzy inference process. The result is a membership value that measures to what extent the fact corresponds to a class according to the feature modeled with

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member-ship function. Koppen and Javier (1997) suggested a novel approach based on use of fuzzy logic, neuro-fuzzy networks and morphological operators for the purpose of retrieving texture features. A neuro-fuzzy network is the new family of neural network that adopts the calculation based on fuzzy rule set. Although the fuzzy logic is one of the new approaches, the areas of applications are very wide: image fusion (Singh et al., 2004), edge detection (Mathur and Ahlawat, 2008), process control, management and decision making, economies and pattern recognition and image classification (Yan and Jamshidi, 2004). A fuzzy is a class of objects whose elements have degrees of membership. The natural description of problems, in linguistic terms, rather than in terms of relationships between precise numerical values is the major advantage of this theory.

3) Genetic Algorithms Genetic algorithms are the new evolutionary hybrid algorithms with an aided ability to automatically select the features form an image.

Wang et al. (2012) have utilized genetic algorithm for feature selection from wavelet based extracted texture feature. Genetic algorithms are combined with cellular neural network (CNN) for the purpose of image texture classification and representation as discussed by (Lin and Shou, 2005), Shou and Lin, 2004). A Cellular neural network is a N-dimensional regular array of cells. Cells are multiple input-single output processors; all described by one or just some few parametric functions.

Conclusion It is well known that texture analysis has its relevance in pattern recognition, image processing and in large number of possible applications. In this paper, various texture analysis methods for image data have been discussed. Statistical and transform based approaches are found to be more frequently adopted by various researchers as they perform better in terms of accuracy and computational complexity when compared with other approaches. Soft computing based approaches are utilised to classify image texture using extracted texture features.

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