In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried ...

In texture analysis the common methods are based on the gray levels of the texture image. However, the use of color information improves the classification accuracy of the colored textures. In the classification of nonhomogenous natural textures, human texture and color perception are important. Therefore, the color space and texture analysis method should be selected to correspond to human vis...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

A methodology for texture analysis termed TEXSCALE is described that involves a hierarchical approach to the problem of texture recognition and image segmentation by texture. TEXSCALE involves the determination of texture class 'tuned' masks that determine whether a texture belongs in a particular texture class, coupled with the use of 'tuned' masks that differentiate between members of the sam...

Local Parameter Histograms (LPH) based on Gaussian Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are...

Textures are one of the basic features in visual searching and computational vision. In the literature, most of the attention has been focussed on the texture features with minimal consideration of the noise models. In this paper we investigated the problem of texture classification from a maximum likelihood perspective. We took into account the texture model, the noise distribution, and the in...

In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was applied with three feature vectors, based on color/gray values, four texture features, and their ...

This thesis investigates texture classification using Non-Parametric Markov Random fields. Texture models using local image descriptors are investigated. Classification performance using such models is then reported upon and the results are used to guide development of future classifiers which take account of scale information within an image. The issues and effects of scale within texture mode...

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one ‘internal texture’ and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combinat...