Texture classification using edge detection and association rules

Abstract

Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association riles can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules.