In this paper we propose a novel feature extraction scheme for texture classi cation, in which the texture features are extracted by a two-level hybrid scheme by integrating two statistical techniques of texture analysis. In the rst step, the low level features are extracted by the Gabor lters, and they are encoded with the feature map indices using the Kohonen's SOFM algorithm. In the next ste...

This paper presents the study on identification and classification of food grains using different color models such as L*a*b, HSV, HSI and YCbCr by combining color and texture features without performing preprocessing. The K-NN and minimum distance classifier are used to identify and classify the different types of food grains using local and global features. Texture and color features are the ...

Texture analysis and classification are usual tasks in pattern recognition. Rock texture is a demanding classification task, because the texture is often non-homogenous. In this paper, we introduce a rock texture classification method, which is based on textural and spectral features of the rock. The spectral features are considered as some color parameters whereas the textural features are cal...

Image retrieval based on texture features is getting unusual concentration because texture is an important feature of natural images. In this paper, we intend to implement texture features extraction technique for content-based image retrieval using fractional integral masks. We propose one general fractional integral mask on eight directions for texture features extraction. Experiments show th...

We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The secon...

This paper introduces a texture features extraction technique for content-based image retrieval using fractional differential operator mask convolution with Cesáro means. We propose one general fractional differential mask on eight directions for texture features extraction. Image retrieval based on texture features is getting unusual concentration because texture is an important feature of nat...

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...

A detailed evaluation of the use of texture features in a query-by-example approach to image retrieval is presented. Three radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view are used. The features were evaluated and tuned on retrieval tasks from the Corel collection and then evaluated and tested on the TRECVID 2003 ...

In industrial applications, product identification is the most common thing now days. To kept in mind that we focus on the classification of our industrial product with the help of its texture using segmentation [7] and offset. Texture plays an important role in identifying the characteristics of an image/product. Image has visual features which are characterized as: (i) domain specific feature...

Background & Aims: Early detection and reliable differentiation of benign and malignant liver tumors could lead to improved cure rate and costs. Ultrasound image (US) is a convenient medical imaging method for interpreting liver tumors. Visual inspection of ultrasound images sometimes is combined with error and needs biopsy to confirm whether a tumor would be benign or malignant. The aim of thi...