Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter

Abstract

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter adaptive coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear image regarding the 2-D second-order Volterra model. Whatever the degree of the nonlinearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification in characterization techniques. The first criterion is proposed to quantify the classification accuracy based on a weighted Euclidean distance classifier. The second criterion is the characterization degree based on the ratio of ";;;;;;;between-class";;;;;;; variances with respect to ";;;;;;;within-class";;;;;;; variances of the estimated coefficients. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric characterization methods even in a noisy context. Key words: Image Analysis, 2-D nonlinear filter, 2-D adaptive filter, texture characterization.

Keywords

• Image Analysis
• 2-D nonlinear filter
• 2-D adaptive filter
• texture characterization