an efficient feature extraction technique for texture learning

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8 , No. 2 , 2010 An Efficient Featu re Extraction T echnique for Texture Learning R. Suguna Research Scholar, Department of Information Technology Madras Institute of Technology, Anna University Chennai- 600 044, Tamil Nadu, India. [email protected] P. Anandhakumar Assistant Professor, Department of Information Tech. Madras Institute of Technology, Anna University Chennai- 600 044, Tamil Nadu, India. [email protected] Abstract— This paper presents a new methodology for discovering features of texture images. Orthonormal Polynomial based Transform is used to extract the features from the images. Using orthonormal polynomial basis function polynomial operators with different sizes are generated. These operators are applied over the images to capture the texture features. The training images are segmented with fixed size blocks and features are extracted from it. The operators are applied over the block and their inner product yields the transform coefficients. These set of transform coefficients form a feature set of a particular texture class. Using clustering technique, a codebook is generated for each class. Then significant class representative vectors are calculated which characterizes the textures. Once the orthonormal basis function of particular size is found, the operators can be realized with few matrix operations and hence the approach is computationally simple. Euclidean Distance measure is used in the classification phase. The transform coefficients have rotation invariant capability. In the training phase the classifier is trained with samples with one particular angle of image and tested with samples at different angles. Texture images are collected from Brodatz album. Experimental results prove that the proposed approach provides good discrimination between the textures. Keywords- Texture Analysis; Orthonormal Transform; codebook generation; Texture Class representatives; Texture Characterization. I. I NTRODUCTION Texture can be regarded as the visual appearance of a surface or material. Textures appear in numerous objects and environments in the universe and they can consist of very different elements. Texture analysis is a basic issue in image processing and computer vision. It is a key problem in many application areas, such as object recognition, remote sensing, content-based image retrieval and so on. A human may describe textured surfaces with adjectives like fine, coarse, smooth or regular. But finding the correlation with mathematical features indicating the same properties is very difficult. We recognize texture when we see it but it is very difficult to define. In computer vision, the visual appearance of the view is captured with digital imaging and stored as image pixels. Texture analysis researchers agree that there is significant variation in intensity levels or colors between nearby pixels and at the limit of resolution there is non- homogeneity. Spatial non-homogeneity of pixels corresponds to the visual texture of the imaged material which may result from physical surface properties such as roughness, for example. Image resolution is important in texture perception, and low-resolution images contain typically very homogenous textures. The appearance of texture depend upon three ingredients: (i) some local ‘order’ is repeated over a region which is large in comparison to the order’s size, (ii) the order consists in the nonrandom arrangement of elementary parts, and (iii) the parts are roughly uniform entities having approximately the same dimensions everywhere within the textured region[1]. Image texture, defined as a function of the spatial variation in pixel intensities (gray values), is useful in a variety of applications and has been a subject of intense study by many researchers. One immediate application of image texture is the recognition of image regions using texture properties. Texture is the most important visual cue in identifying these types of homogeneous regions. This is called texture classification. The goal of texture classification then is to produce a classification map of the input image where each uniform textured region is identified with the texture class it belongs to [2]. Texture analysis methods have been utilized in a variety of application domains. Texture plays an important role in automated inspection, medical image processing, document processing and remote sensing. In the detection of defects in texture images, most applications have been in the domain of textile inspection. Some diseases, such as interstitial fibrosis , affect the lungs in such a manner that the resulting changes in the X-ray images are texture changes as opposed to clearly delineated lesions. In such applications, texture analysis methods are ideally suited for these images. Texture plays a significant role in document processing and character recognition . The text regions in a document are characterized by their high frequency content. Texture analysis has been extensively used to classify remotely sensed images. Land use classification where homogeneous regions with different types of terrains (such as wheat, bodies of water, urban regions, etc.) need to be identified is an important 21 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

an efficient feature extraction technique for texture learning

Documents