This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. ...

Texture is the term used to characterize the surface of a given object or phenomenon and is an important feature used in image processing and pattern recognition. Our aim is to compare various Texture analyzing methods and compare the results based on time complexity and accuracy of classification. The project describes texture classification using Wavelet Transform and Co occurrence Matrix. Co...

To develop a noise-insensitive texture classification algorithm for both optical and underwater sidescan sonar images, we study the multichannel texture classification algorithm that uses the wavelet packet transform and Fourier transform. The approach uses a multilevel dominant eigenvector estimation algorithm and statistical distance measures to combine and select frequency channel features o...

Local graylevel dependencies of natural images can be modelled by means of cooccurrence matrices containing joint probabilities of graylevel pairs. Texture, however, is a resolution–dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus li...

The performance of Support Vector Machines (SVMs) is highly dependent on the choice of a kernel function suited to the problem at hand. In particular, the kernel implicitly performs a feature selection which is the most important stage in any texture classification algorithm. In this work a new Gabor filter based kernel for texture classification with SVMs is proposed. The proposed kernel funct...

Image classification can benefit from incorporating texture by enabling an increased number of classes and improving thematic accuracy. Incorporating texture also involves special attention in a number of aspects that range from the texture source to the evaluation of accuracy through pre-processing, training strategy and choosing a texture extraction paradigm and a classifier. Without special ...

This paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. Experimental results with textured images of outdoor scenes show that the proposed technique yields lower classification errors than widely recognized texture classifiers based on speci...

Texture is one of the important characteristics used in identifying objects. Texture analysis plays an important role in image analysis and pattern recognition. In this paper a new set of features obtained from the 1-D MRT is shown to be useful for texture analysis. Experiments performed on 30 samples of Brodatz texture images indicate that these features can be used for texture classification.

We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixtur...

This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can de...