PIXEL-BASED TEXTURE CLASSIFICATION BY INTEGRATION OF MULTIPLE FEATURE EXTRACTION METHODS EVALUATED OVER MULTISIZED WINDOWS

Abstract

This paper presents a pixel-based texture classifier oriented to the identification of texture models that can be present in an input image, given a set of models known in advance. The proposed methodology is based on the integration of texture features generated by texture methods that belong to different families, which are evaluated over multiple windows of different sizes. This is a novelty with respect to the current texture classifiers, which are based on specific families of texture methods evaluated over single windows of a size defined empirically. Experiments show that this integration strategy produces better results than classical texture classifiers based on specific families of texture methods.