Feature Fusion Based on Dempster-shafer's Evidential Reasoning for I Mage Texture Classification*

A new multi-feature fusion technique based on Dempster-Shafer's evidential reasoning for classification of image texture is presented. The proposed technique is divided into three main steps. In the first step, the fractal dimension and gray co-occurrence matrix entropy are extracted from a texture image. In the second step, we focus on how to design a probability assignment function m(A) representing the exact belief in the proposition A depicted by one of features. A combining rule, which synthesizes probability assignment functions representing the fused information, is proposed based on Dempster-Shafer's evidential reasoning. The formulas for calculating the belief function Belief(A), the plausibility function Plausibility (A) and uncertainty probability are given. In the decisive step in which image texture is classified, a set of decision rules is provided. An example is provided, and the performance is investigated with some aerial photos. Texture classification is considered, with the following classes: inhabitant area, water field, grassland and woodland. As a reference for evaluating the performance of multi-feature fusion technique based on Dempster-Shafer's evidential reasoning in texture classification, classification accuracies using the single-feature and fused features are calculated. Compared with the results obtained from the single feature, the results obtained from multi-feature fusion indicate the multi-feature fusion technique based on Dempster-Shafer's evidential reasoning for classification is stable and reliable, and efficiently improves the accuracy of classification. The project supported by the National Surveying and Mapping Fund of China