<title>Texture classification by a two-level hybrid scheme</title>

Abstract

In this paper we propose a novel feature extraction scheme for texture classification, in which the texture features are extracted by a two-level hybrid scheme by integrating two statistical techniques of texture analysis. In the first step, the low level features are extracted by the Gabor filters, and they are encoded with the feature map indices using the Kohonen's SOFM algorithm. In the next step, the encoded feature images are processed by the Gabor filters, Gaussian Markov random fields (GMRF), and Grey level co-occurence matrix (GLCM) methods to extract the high level features. By integrating two methods of texture analysis in a cascaded manner, we obtained the texture features that achieved a high accuracy for the classification of texture patterns. The proposed schemes were tested on the real micro-textures, and the Gabor-GMRF scheme achieved 10% increase of the recognition rate compared to the result obtained by the simple Gabor filtering.

Structured Knowledge Representation for Image Retrieval

“…Texture. We extract texture information for each region r i with a method based on the work by Pok and Liu (1999). Following this approach, we extract texture features convolving the original grey level image I(x, y) with a bank of Gabor filters, having the following impulse response: x+V y) where (U, V ) represents the filter location in the frequency-domain, λ is the central frequency, σ is the scale factor, and θ the orientation, defined as:…”

“…Texture. We extract texture information for each region r i with a method based on the work by Pok and Liu (1999). Following this approach, we extract texture features convolving the original grey level image I(x, y) with a bank of Gabor filters, having the following impulse response:…”