Image Processing

Topics: Wavelet, Statistics, Image processing Pages: 22 (3681 words) Published: April 12, 2013
International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) Volume 2, Issue 1, March 2011


Analysis and Comparison of Texture Features for Content Based Image Retrieval S.Selvarajah 1 and S.R. Kodituwakku 2

Department of Physical Science, Vavuniya Campus of the University of Jaffna, Vavuniya, Sri Lanka 2

Department of Statistics & Computer Science, University of Peradeniya, Sri Lanka. {,}

Abstract: Texture is one of the important features used in CBIR systems. The methods of characterizing texture fall into two major categories: Statistical and Structural. An experimental comparison of a number of different texture features for content-based image retrieval is presented in this paper. The primary goal is to determine which texture feature or combination of texture features is most efficient in representing the spatial distribution of images. In this paper, we analyze and evaluate both Statistical and Structural texture features. For the experiments, publicly available image databases are used. Analysis and comparison of individual texture features and combined texture features are presented. Keywords: Image Retrieval, Feature Representation, First

second order statistics, higher order statistics and multiresolution techniques such as wavelet transform. In this paper, we try to analysis and compare the performances of both statistical and structural approaches. Among the statistical features the first-order statistics and the secondorder statistics based features are considered. Twodimensional wavelet transform and Gabor transform are considered as structural features. This paper presents the evaluation of the effectiveness and efficiency of such texture features in CBIR.

2. Methods and Materials
In order to evaluate the effectiveness and efficiency of texture features the following materials and methods are used. 2.1 Materials

Order Statistics, Second Order Statistics, Gabor Transform, Wavelet Transforms.

1. Introduction
In CBIR systems [1]-[9], a feature is a characteristic that can capture a certain visual property of an image either globally for the entire image or locally for regions or objects [5]. Texture is the main feature utilized in image processing and computer vision to characterize the surface and structure of a given object or region. Since an image is made up of pixels, texture can be defined as an entity consisting of mutually related pixels and group of pixels. This group of pixels is called as texture primitives or texture elements (texels). As the texture is a quantitative measure of the arrangement of intensities in a region, the methods to characterize texture fall into two major categories: Statistical and Structural. Statistical methods characterize texture by the statistical distribution of the image intensity. Spatial distribution of gray values is one of the defining qualities of texture. Statistical methods analyze the spatial distribution of gray values, by computing local features at each point in the image, and deriving a set of statistics from the distributions of the local features [10]. Structural methods describe texture by identifying structural primitives and their placement rules. They are suitable for textures where their spatial sizes can be described using a large variety of properties. Gabor Filters and Wavelet Transforms are widely used for describing structural primitives [11], [12]. Many approaches have been proposed for texture based image retrieval. These range from first order statistics,

To analyze and evaluate the performance of texture descriptors, following structural and statistical features are considered. 2.1.1 Structural Methods Two different structural methods are considered: twodimensional wavelet transform and Gabor transform. (a) Gabor Transform Gabor filters are a group of wavelets. For a given image I(x, y) with size M×, its discrete Gabor wavelet transform is given by...
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