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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 An Efficient Feature Extraction Technique for Texture Learning R. Suguna P. Anandhakumar Research Scholar, Department of Information Technology Assistant Professor, Department of Information Tech. Madras Institute of Technology, Anna University Madras Institute of Technology, Anna University Chennai- 600 044, Tamil Nadu, India. Chennai- 600 044, Tamil Nadu, India. hitec_suguna@hotmail.com anandh@annauniv.edu Abstract— This paper presents a new methodology for pixels. Texture analysis researchers agree that there is discovering features of texture images. Orthonormal significant variation in intensity levels or colors between Polynomial based Transform is used to extract the features nearby pixels and at the limit of resolution there is non- from the images. Using orthonormal polynomial basis homogeneity. Spatial non-homogeneity of pixels corresponds function polynomial operators with different sizes are to the visual texture of the imaged material which may result generated. These operators are applied over the images to from physical surface properties such as roughness, for capture the texture features. The training images are example. Image resolution is important in texture perception, segmented with fixed size blocks and features are extracted and low-resolution images contain typically very homogenous textures. from it. The operators are applied over the block and their inner product yields the transform coefficients. These set The appearance of texture depend upon three ingredients: of transform coefficients form a feature set of a particular (i) some local ‘order’ is repeated over a region which is large in texture class. Using clustering technique, a codebook is comparison to the order’s size, (ii) the order consists in the generated for each class. Then significant class nonrandom arrangement of elementary parts, and (iii) the parts representative vectors are calculated which characterizes are roughly uniform entities having approximately the same the textures. Once the orthonormal basis function of dimensions everywhere within the textured region[1]. particular size is found, the operators can be realized with Image texture, defined as a function of the spatial variation few matrix operations and hence the approach is in pixel intensities (gray values), is useful in a variety of computationally simple. Euclidean Distance measure is applications and has been a subject of intense study by many used in the classification phase. The transform coefficients researchers. One immediate application of image texture is the have rotation invariant capability. In the training phase recognition of image regions using texture properties. Texture the classifier is trained with samples with one particular is the most important visual cue in identifying these types of angle of image and tested with samples at different angles. homogeneous regions. This is called texture classification. The Texture images are collected from Brodatz album. goal of texture classification then is to produce a classification Experimental results prove that the proposed approach map of the input image where each uniform textured region is provides good discrimination between the textures. identified with the texture class it belongs to [2]. Texture analysis methods have been utilized in a variety Keywords- Texture Analysis; Orthonormal Transform; codebook generation; Texture Class representatives; Texture of application domains. Texture plays an important role in Characterization. automated inspection, medical image processing, document processing and remote sensing. In the detection of defects in I. INTRODUCTION texture images, most applications have been in the domain Texture can be regarded as the visual appearance of a of textile inspection. Some diseases, such as interstitial surface or material. Textures appear in numerous objects and fibrosis, affect the lungs in such a manner that the resulting environments in the universe and they can consist of very changes in the X-ray images are texture changes as opposed different elements. Texture analysis is a basic issue in image to clearly delineated lesions. In such applications, texture processing and computer vision. It is a key problem in many analysis methods are ideally suited for these images. application areas, such as object recognition, remote sensing, content-based image retrieval and so on. A human may Texture plays a significant role in document processing and describe textured surfaces with adjectives like fine, coarse, character recognition. The text regions in a document are smooth or regular. But finding the correlation with characterized by their high frequency content. Texture mathematical features indicating the same properties is very analysis has been extensively used to classify remotely sensed difficult. We recognize texture when we see it but it is very images. Land use classification where homogeneous regions difficult to define. In computer vision, the visual appearance of with different types of terrains (such as wheat, bodies of water, the view is captured with digital imaging and stored as image urban regions, etc.) need to be identified is an important 21 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 application. Haralick et al. [3] used gray level co-occurrence the surface and how they are located. Stochastic textures are features to analyze remotely sensed images. usually natural and consist of randomly distributed texture elements, which again can be, for example, lines or curves (e.g. Since we are interested in interpretation of images we can tree bark). The analysis of these kinds of textures is based on define texture as the characteristic variation in intensity of a statistical properties of image pixels and regions. The above region of an image which should allow us to recognize and categorization of textures is not the only possible one; there describe it and outline its boundaries. The degrees of exist several others as well, for example, artificial vs. natural or randomness and of regularity will be the key measure when micro textures vs. macro textures. Regardless of the characterizing a texture. In texture analysis the similar textural categorization, texture analysis methods try to describe the elements that are replicated over a region of the image are properties of the textures in a proper way. It depends on the called texels. This factor leads us to characterize textures in the applications what kind of properties should be sought from the following ways: textures under inspection and how to do that. This is rarely an • The texels will have various sizes and degrees of easy task. uniformity One of the major problems when developing texture • The texels will be oriented in various directions measures is to include invariant properties in the features. It is very common in a real-world environment that, for example, • The texels will be spaced at varying distances in different the illumination changes over time, and causes variations in the directions texture appearance. Texture primitives can also rotate and • The contrast will have various magnitudes and variations locate in many different ways, which also causes problems. On • Various amounts of background may be visible between the other hand, if the features are too invariant, they might not texels be discriminative enough. • The variations composing the texture may each have II. TEXTURE MODELS varying degrees of regularity Image texture has a number of perceived qualities which It is quite clear that a texture is a complicated entity to play an important role in describing texture. One of the measure. The reason is primarily that many parameters are defining qualities of texture is the spatial distribution of gray likely to be required to characterize it. Characterization of values. The use of statistical features is therefore one of the textured materials is usually very difficult and the goal of early methods proposed in the machine vision literature. characterization depends on the application. In general, the aim The gray-level co-occurrence matrix approach is based on is to give a description of analyzed material, which can be, for studies of the statistics of pixel intensity distributions. The example, the classification result for a finite number of classes early paper by Haralick et al.[4] presented 14 texture measures or visual exposition of the surfaces. It gives additional and these were used successfully for classification of many information compared only to color or shape measurements of types of materials for example, wood, corn, grass and water. the objects. Sometimes it is not even possible to obtain color However, Conners and Harlow [5] found that only five of these information at all, as in night vision with infrared cameras. measures were normally used, viz. “energy”, “entropy”, Color measurements are usually more sensitive to varying “correlation”, “local homogeneity”, and “inertia”. The size of illumination conditions than texture, making them harder to use the co-occurrence matrix is high and suitable choice of d in demanding environments like outdoor conditions. Therefore (distance) and θ (angle) has to be made to get relevant features. texture measures can be very useful in many real-world applications, including, for example, outdoor scene image A novel texture energy approach is presented by Laws [6]. analysis. This involved the application of simple filters to digital images. The basic filters he used were common Gaussian, edge To exploit texture in applications, the measures should be detector, and Laplacian-type filters and were designed to accurate in detecting different texture structures, but still be highlight points of high “texture energy” in the image. Ade invariant or robust with varying conditions that affect the investigated the theory underlying Laws’ approach and texture appearance. Computational complexity should not be developed a revised rationale in terms of Eigen filters [7]. Each too high to preserve realistic use of the methods. Different eigenvalue gives the part of the variance of the original image applications set various requirements on the texture analysis that can be extracted by the corresponding filter. The filters that methods, and usually selection of measures is done with respect give rise to low variances can be taken to be relatively to the specific application. unimportant for texture recognition. Typically textures and the analysis methods related to them The structural models of texture assume that textures are are divided into two main categories with different computational approaches: the stochastic and the structural composed of texture primitives. The texture is produced by methods. Structural textures are often man-made with a very the placement of these primitives according to certain regular appearance consisting, for example, of line or square placement rules. This class of algorithms, in general, is primitive patterns that are systematically located on the surface limited in power unless one is dealing with very regular (e.g. brick walls). In structural texture analysis the properties textures. Structural texture analysis consists of two major and the appearance of the textures are described with different steps: (a) extraction of the texture elements, and (b) inference rules that specify what kind of primitive elements there are in of the placement rule. An approach to model the texture by 22 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 structural means is described by Fu [8]. In this approach the Local frequency analysis has been used for texture analysis. texture image is regarded as texture primitives arranged One of the best known methods uses Gabor filters and is based according to a placement rule. The primitive can be as simple on the magnitude information [14]. Phase information has been as a single pixel that can take a gray value, but it is usually a used in [15] and histograms together with spectral information collection of pixels. The placement rule is defined by a tree in [16]. Ojala T & Pietikäinen M [17] proposed a grammar. A texture is then viewed as a string in the language multichannel approach to texture description by approximating defined by the grammar whose terminal symbols are the texture joint occurrences of multiple features with marginal primitives. An advantage of this method is that it can be used distributions, as 1-D histograms, and combining similarity for texture generation as well as texture analysis. scores for 1-D histograms into an aggregate similarity score. Ojala T introduced a generalized approach to the gray scale and Model based texture analysis methods are based on the rotation invariant texture classification method based on local construction of an image model that can be used not only to binary patterns [18]. The current status of a new initiative describe texture, but also to synthesize it. The model aimed at developing a versatile framework and image database parameters capture the essential perceived qualities of texture. for empirical evaluation of texture analysis algorithms is Markov random fields (MRFs) have been popular for modeling presented by him. Another frequently used approach in texture images. They are able to capture the local (spatial) contextual description is using distributions of quantized filter responses information in an image. These models assume that the to characterize the texture (Leung and Malik), (Varma and intensity at each pixel in the image depends on the intensities Zisserman) [19] [20]. Ahonen T, proved that the local binary of only the neighboring pixels. Many natural surfaces have a pattern operator can be seen as a filter operator based on local statistical quality of roughness and self-similarity at different derivative filters at different orientations and a special vector scales. Fractals are very useful and have become popular in quantization function [21]. modeling these properties in image processing. A rotation invariant extension to the blur insensitive local However, the majority of existing texture analysis phase quantization texture descriptor is presented by Ojansivu methods makes the explicit or implicit assumption that texture V [22]. images are acquired from the same viewpoint (e.g. the same scale and orientation). This gives a limitation of these methods. Unitary Transformations are also used to represent the In many practical applications, it is very difficult or impossible images. The simple and powerful class of transform coding is to ensure that images captured have the same translations, linear block transform coding, where the entire image is rotations or scaling between each other. Texture analysis partitioned into a number of non-overlapping blocks and then should be ideally invariant to viewpoints. Furthermore, based the transformation is applied to yield transform coefficients. on the cognitive theory and our own perceptive experience, This is necessitated because of the fact that the original pixel given a texture image, no matter how it is changed under values of the image are highly correlated. A framework using translation, rotation and scaling or even perspective distortion, orthogonal polynomials for edge detection and texture analysis it is always perceived as the same texture image by a human is presented in [23] [24]. observer. Invariant texture analysis is thus highly desirable from both the practical and theoretical viewpoint. III. ORTHONORMAL POLYNOMIAL TRANSFORM Recent developments include the work with A linear 2-D image formation system usually considered automated visual inspection in work. Ojala et al., [9] and around a Cartesian coordinate separable, blurring, point spread Manthalkar et al., [10] aimed at rotation invariant texture operator in which the image I results in the superposition of the classification. Pun and Lee [11] aims at scale invariance. Davis point source of impulse weighted by the value of the object f. [12] describes a new tool (called polarogram) for image texture Expressing the object function f in terms of derivatives of the analysis and used it to get invariant texture features. In Davis’s image function I relative to its Cartesian coordinates is very method, the co-occurrence matrix of a texture image must be useful for analyzing the image. The point spread function M(x, computed prior to the polarograms. However, it is well known y) can be considered to be real valued function defined for (x, that a texture image can produce a set of co-occurrence y) € X x Y, where X and Y are ordered subsets of real values. matrices due to the different values of a and d. This also results In case of gray-level image of size (n x n) where X (rows) in a set of polarograms corresponding to a texture. Only one consists of a finite set, which for convenience labeled as {0, 1, polarogram is not enough to describe a texture image. How 2, … ,n-1}, the function M(x, y) reduces to a sequence of many polarograms are required to describe a texture image functions. remains an open problem. The polar grid is also used by Mayorga and Ludeman [13] for rotation invariant texture Μ , (τ), τ=0,1,...ν−1 (1) analysis. The features are extracted on the texture edge statistics obtained through directional derivatives among The linear two dimensional can be defined by the point circularly layered data. Two sets of invariant features are used spread operator M(x,y), (M(i,t) = ui(t)) as shown in equation 2. for texture classification. The first set is obtained by computing the circularly averaged differences in the gray level between , , , , (2) pixels. The second computes the correlation function along circular levels. It is demonstrated by many recent publications that Zernike moments perform well in practice to obtain geometric invariance. 23 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 Considering both X and Y to be a finite set of values {0, 1, (a) If each pair of distinct vectors from S is orthogonal then 2, …. ,n-1}, equation (2) can be written in matrix notation as we call S an orthogonal set. follows (b) If S is an orthogonal set and each of the vectors in S also has a norm of 1 then we call S an orthonormal set. | | | | | | (3) To enforce orthonormal property, divide each vector by its where ⊗ is the outer product, are matrices norm. Suppose , , forms an orthogonal set. Then, , , , 0. Any vector v can be turned arranged in the dictionary sequence, | | is the image, are into a vector with norm 1 by dividing by its norm as follows, the coefficients of transformation and the point spread operator | | is (10) … … To convert S to have orthonormal property, divide each . vector by its norm. | | . (4) . , 1, 2, 3 (11) … We consider the set of orthogonal polynomials After finding the orthonormal basis function, the operators , ,…, of degrees 0, 1, 2, …, n-1 respectively are generated by applying outer product. For an orthonormal to construct the polynomial operators of different sizes from basis function of size n, operators are generated. Applying equation (4) for n ≥ 2 and . The generating formula for the operators over the block of the image we get transform the polynomials is as follows. coefficients. IV. METHODOLOGY 1 (5) Sample Images representing different Textures are , 1, collected. We collected the images from Outex Texture Database. Each image is of size 128 x 128. Images of each texture are partitioned into two groups as Training Set and Test where Set. , ∑ The process involved in capturing the texture b n (6) , ∑ characterization is depicted in Figure-1. Each training image is partitioned into non-overlapping blocks of size M*M. We have and chosen M = 4. Features are extracted from each block using orthonormal polynomial based transform as described in ∑ (7) section 3. From each block a k-dimensional feature vector is generated. A codebook is built for each concept classes. The algorithm for construction of codebook is discussed below. Considering the range of values of to be , 1,2,3, … , we get A. Codebook Generation Algorithm Input: Training Images of Texture Ti (8) Output: Codebook of the Texture Ti 1. Read the image Tr(m) from the Texture Class Ti, where m=1,2,…M; M denotes the number of training ∑ (9) images in Ti and i=1,2,…L; L denotes the number of Textures. Size of Tr(m) is 128x128. We can construct point-spread operators | | of different 2. Each image is partitioned into p x p blocks and we size from equation (4) using the above orthogonal polynomials have P blocks for each training image, p=4. for 2 and . 3. For each block apply Orthonormal Based transform by The orthogonal basis functions for n=2 and n=3 are given using a set of (pxp) polynomial operators and extract below. the feature coefficients. Inner product between a 1 1 1 polynomial operator and image block results in a 1 1 transform coefficient. We get p2 coefficients for each 1 0 2 1 1 block. 1 1 1 Orthonormal basis functions can be derived from 4. Rearrange the feature coefficients into 1-D array in orthogonal sets. Suppose that S is a set of vectors in an inner descending sequence. product space. 24 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 Figure 1. Process involved in Texture Characterization are used for training the texture classifier, which is then tested with the samples of the other rotation angles. 5. Take only d coefficients to form the feature vector z, where z = {z(j), j=1,2,…,d; d<k}. A. Image Data and Experimental Setup 6. From P blocks get P x d coefficients. The image data included 12 textures from the Brodatz album. Textures are presented at 6 different rotation angles (0, 7. Repeat 2-6 for all images in Ti and collect the z vectors. 30, 60, 90, 120, and 150). For each texture class there were 16 images for each class and angle (hence 1248 images in total). Apply clustering technique, to cluster the feature vectors of Each texture class comprises following subsets of images: 16 Ti. The number clusters decides the codebook size. The mean 'original' images, 16 images rotated at 300 , 16 images rotated at of the clusters form the code vectors. 600, 16 images rotated at 900, 16 images rotated at 1200and16 B. Building Class Representative Vector images rotated at 1500. The size of each image is 128x128. Input: Images of size N x N, Texture codebook The texture classes considered for our study are shown in Output: Class Representative Vector Ri. Figure. 2. The texture classes are divided into two sets. Texture Set-1 contains structural textures (regular patterns) and Texture 1. For each image in Ti, generate the code indices Set-2 contains stochastic textures (irregular patterns). associated with the corresponding codebook. Texture Set-1 includes {bark, brick, bubbles, raffia, straw, 2. Find the number of occurrences in each code index for weave}. Texture Set-2 includes {grass, leather, pigskin, sand, each image. water, wool}. 3. Compute the mean of occurrences to generate class representative vector Ri, where i=1,2,…L, where L is the number of Textures. The statistical features of the texture class are studied first. The mean and variance of the texture classes are found and 4. Repeat 1-3 for all Ti. depicted in Figure-3 to Figure 6. C. Texture Classification Given any texture image this phase determines to which texture class the image is relevant. Images from the Test set are partitioned into non-overlapping blocks of size M*M. Features are extracted using orthonormal polynomial Transform. Consulting the codebooks, code indices are generated and the corresponding input representative vector is formed. Compute the distance di between the Class Representative vector Ri and input image representative vector IRi for Ti. Euclidean distance is used for similarity measure. di = dist(IRi, Ri) Find min (di) to obtain the Texture class. V. RESULTS AND DISCUSSION We demonstrate the performance of our approach with the proposed Transform coefficients with texture image data that have been used in recent studies on rotation invariant texture Figure 2. Sample Images of Textures classification. Since the data included samples from several rotation angles, we also present results for a more challenging setup, where the samples of just one particular rotation angle 25 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 B. Contribution of Transform Coefficients Each Texture class with rotation angle 0 is taken for training. Other images are used for Testing. For each Texture class a code book is generated with the training samples. A Class Representative Vector is estimated. Figure 7 and Figure 8 shows the representatives of Textures. Figure 3. Mean of Structural Textures Figure 7. Class Representatives of Structural Textures Figure 4. Mean of Stochastic Textures Figure 8. Class Representatives of Stochastic Textures Table 1 and Table 2 presents results for a the challenging experimental setup where the classifier is trained with samples of just one rotation angle and tested with samples of other rotation angles. Figure 5. Variance of Structural Textures Classification Accuracy (%) for different Training Texture angles 300 600 900 1200 1500 Bark 86.6 68.75 86.6 75.0 68.75 Brick 75.0 86.6 87.5 75.0 86.6 Bubbles 93.75 93.75 100 100 100 Raffia 100 93.75 87.5 87.5 87.5 Straw 56.25 62.5 68.75 56.25 62.5 Weave 93.75 100 100 100 93.75 Figure 6. Variance of Stochastic Textures Table 1 Classification Accuracies (%) of Structural Textures trained with One rotation angle (00) and Tested with other versions 26 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 The overall performance of Structured and Stochastic Textures is reported in Figure 11 and Figure 12. If the mean Classification Accuracy (%) for different Training angles difference between the textures is less, then their classification Texture 300 600 900 1200 1500 performance degrades. Grass 100 100 100 93.75 93.75 Leather 87.5 93.75 87.5 93.75 93.75 Pigskin 87.5 87.5 93.75 75.0 68.75 Sand 75.0 75.0 68.75 68.75 75.0 Water 100 93.75 93.75 87.5 87.5 Wool 86.6 86.6 62.5 68.75 75 Table 2 Classification Accuracies (%) of stochastic Textures trained with One rotation angle (00) and Tested with other versions Figure 11. Overall Classification Performance of Structural Textures It is observed that in Structural Textures, Bark is misclassified as Straw and few as Brick. Brick is misclassified as Raffia. Straw is misclassified as Bark and Bubbles. In the case of Stochastic Textures Sand is misclassified as pigskin. Wool is misclassified as Pigskin and sand. Compared to structural Textures the performance of stochastic Textures is good. The performance of Structured Textures and Stochastic Textures are shown in Figure 9 and Figure 10. Figure 12. Overall Classification Performance of Stochastic Textures We have also compared the performance of our feature extraction method with other approaches. Table 3 shows the comparative study with other Texture models. Figure 9. Classification Performance of Structural Textures Texture model Recognition rate in % Co occurrence matrix 78.6 Autocorrelation method 76.1 Laws Texture measure 82.2 Orthonormal Transformed Feature 89.2 Extraction Table 3 Performance of various Texture measures in classication Figure 10. Classification Performance of Stochastic Textures 27 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, 2010 VI. CONCLUSION 2001 Proceedings, Lecture Notes In Computer Science 2013, Springer, 397 - 406. An efficient way of extracting features from textures is [19] Leung T And Malik J (2001) Representing And Recognizing The Visual presented. From the orthonormal basis, new operators are Appearance Of Materials Using Three Dimensional Textons, Int. J. generated. These operators perform well in characterizing the Comput. Vision, 43(1):29– 44. textures. The operator can be used for gray-scale and rotation [20] Varma M And Zisserman A (2005) A Statistical Approach To Texture invariant texture classification. Experimental results are Classification From Single Images, International Journal Of Computer appreciable where the original version of image samples are Vision, 62(1–2):61–81. used for learning and tested for different rotation angles. [21] Ahonen T & Pietikäinen M (2008) A Framework For Analyzing Texture Descriptors”, Proc. Third International Conference On Computational simplicity is another advantage since the Computer Vision Theory And Applications (Visapp 2008), Madeira, operator is evaluated by computing the inner product. This Portugal, 1:507-512. facilitates less time for implementation. The efficiency can be [22] Ojansivu V & Heikkilä J (2008) A Method For Blur And Affine further improved by varying the codebook size and the Invariant Object Recognition Using Phase-Only Bispectrum, Proc. dimension of feature vectors. Image Analysis And Recognition (Iciar 2008), Póvoa De Varzim, Portugal, 5112:527-536. 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