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...

A content-based image retrieval algorithm is proposed after researching feature extraction of image texture, shape feature extraction and relevance feedback algorithm. Fourier transform is used in the feature extraction of the texture. Boundary moments to detect the image boundaries is used in the feature extraction of the shape, similarity measuring function is used in image similarity match. ...

The retrieval using single feature has a certain limitation, which fails to comprehensively describe an image. Aiming at such retrieval defect, this paper proposes an image retrieval method integrating color and texture. Firstly, carry out image segmentation with uniformly-spaced method, and then extract color feature of each segmentation with weighting processing done; and then, extract textur...

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis (PCA) is used to reduce the feature ...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward oating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classi cation based on SAR satellite images using four di erent texture models. Pooling feat...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling ...

The computational complexity of a texture classification algorithm is limited by the dimensionality of the feature space. Although finding the optimal feature subset is a NP-hard problem [1], a feature selection algorithm that can reduce the dimensionality of problem is often desirable. In this paper, we report work on a feature selection algorithm for texture classification using two subband f...

This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse imag...

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...

Most Content-Based Image Retrieval (CBIR) systems employ color as primary feature with texture and shape as secondary features. Very few systems exploit spatial features. None of the available systems combines all three visual features, texture, shape and location, for organization and retrieval. Moreover relatively few systems use Gabor filters in texture extraction, despite the widely acclaim...