Image Processing
International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) Volume 2, Issue 1, March 2011
108
Analysis and Comparison of Texture Features for Content Based Image Retrieval
S.Selvarajah 1 and S.R. Kodituwakku 2
1
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.
{shakeelas@mail.vau.jfn.ac.lk, salukak@pdn.ac.lk}
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
108
Analysis and Comparison of Texture Features for Content Based Image Retrieval
S.Selvarajah 1 and S.R. Kodituwakku 2
1
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.
{shakeelas@mail.vau.jfn.ac.lk, salukak@pdn.ac.lk}
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