Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray leve...

Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern and Text...

Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and class...

Texture classification is used in various pattern recognition applications that possess feature-liked appearance. This paper aims to compile the recent trends on the usage of feature extraction and classification methods used in the research of texture classification as well as the texture datasets used for the experiments. The study shows that the signal processing methods, such as Gabor filte...

A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in exper...

A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving microfeatures. From these microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and...

A fast, reliable, and objective system for computerized classification of color textured perthite images is proposed. Computerized classification of perthite textures enables large scale comparative perthite texture studies. In order to locate a perthite crystal’s borders, color and texture features are combined in a pixel classification segmentation operation, followed by probabilistic relaxat...

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, i...

A new approach to texture characterization from dynamic CT scans of the liver is presented. Images with the same slice position and corresponding to three typical acquisition phases are analyzed simultaneously. Thereby texture evolution during the propagation of contrast product is taken into account. The method is applied to recognizing hepatic primary tumors. Experiments with various sets of ...

In this paper, we develop a new approach for texture classification independent of affine transforms. Based on spectral representation of texture images under affine transform, anisotropic scale invariant signatures of orientation spectrum distribution are extracted. Peaks distribution vector (PDV) obtained on the distribution of these signatures captures texture properties invariant to affine ...