This paper proposes an efficient parallel approach to texture classification for image retrieval. The idea behind this method is to pre-extract texture features in terms of texture energy measurement associated with a 'tuned' mask and store them in a multiscale and multi-orientation texture class database via a two-dimensional linked list for query. Thus, each texture class sample in the databa...

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives...

This paper presents a new texture analysis method based on structural properties. The texture features extracted using this algorithm are invariant to affine transform (including rotation, translation, scaling, and skewing). Affine invariant structural properties are derived based on texel areas. An area-ratio map utilizing these properties is introduced to characterize texture images. Histogra...

In this paper, we present a theoretically and computationally simple but efficient approach for rotation invariant texture classification. This method is based on new texture signatures extracted from spectrum. Rotation invariant texture features are obtained based on the extension of the derived signatures. The features are tested with 1000 randomly rotated samples of 20 Brodatz texture classe...

Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral his...

A large number of texture classification approaches have been developed in the past but most of these studies target gray-level textures. In this paper, supervised classification of color textures is considered. Several different Multispectral Random Field models are used to characterize the texture. The classifying features are based on the estimated parameters of these model and functions def...

One of the fundamental issues in image processing and machine vision is texture, specifically texture feature extraction, classification and abnormality detection. This thesis is concerned with the analysis and classification of natural and random textures, where the building elements and the structure of texture are not clearly determinable, hence statistical and signal processing approaches a...

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes. In this paper, we propose a novel approach based on the 2D and semi 3D texture feature coding method (TFCM) for color texture classification. While 2D TFCM features are extracted on gray scale converted color texture images, the se...

Texture classification is one of the problems in the field of texture analysis. In this paper an efficient method of texture classification using Gabor transform is proposed, which considers the effect of rotation and scale variances of texture images. Due to its optimal localization properties in both spatial and frequency domain, the Gabor transform has been recognized as a very useful tool i...