A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumul...

Image textures are groups of metrics computed to classify the captured texture of images. It reveals the information about the spatial orientation of color or gray level intensities in the images or specific regions of the images. The image texture classification of plant leaves is considered in this paper because of the extinction risk of various plants. An efficient plant leaf identification ...

one of the effective tools for identifying land resources and assign them to the best and most profitable forms of land productivity, susceptibility of agro-ecological zoning . in this study agro-ecological zoning of satellite imagery and gis were used. after agro-climatic zoning (based on isorain, isothermal and length of growing period maps) and agro-edaphic zoning (based on soil, slope and l...

One of the enduring problems in remote sensing analysis is how to exploit image texture [2]. A central question in texture analysis is the scale of the texture features analyzed, as determined by the size of the texture kernel (moving window). In a classic study, 90% of texture variability was found to be accounted for by the size of the kernel, compared to only 7% from the texture algorithm [3...

In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning similarity helps to alleviate this perceptual inconsistency. For textures, psychological experiment...

A fast and robust type of unsupervised multispectral texture segmentation method with unknown number of classes is presented. Single decorrelated monospectral texture factors are represented by four local autoregressive random field models recursively evaluated for each pixel and for each spectral band. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with...

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probab...

This paper deals with the combined use of Local Binary Pattern (LBP) features and a Self-Organizing Map (SOM) in texture classification. With this approach, the unsupervised learning and visualization capabilities of a SOM are utilized with highly efficient histogram-based texture features. In addition to the Euclidean distance normally used with a SOM, an information theoretic log-likelihood (...

Based on the support vector machine (SVM) tools and multiple kernel method, the combinations of kernel functions were mainly discussed. The construction method of image differencing kernel with multi-feature (spectral feature and textural feature) has been developed. Through this method and weighting of the categories’ samples, the improved SVM change detection model has been proposed, which co...

A method for finding all images from the same category as a given query image (termed categorization) using texture features is presented. The hypothesis that two images that are similar in texture are likely to belong to the same category, is examined. A new texture feature termed N M -gram is presented. It is based on theN -gram technique that is commonly used for text similarity. The process...