<title>Development of criteria to compare model-based texture analysis methods</title>

Abstract

Texture is an important property useful for image segmentation and the inference of 3-D information in the scene. Many approaches were proposed for analyzing textures. Among them are feature-based approaches and model-based approaches. In a feature-based environment various textural features are extracted from each textured image(or subimage) and are used to classify or discriminate given textures i. e. no explicit consideration of models is taken into account and thus the generation aspect is ignored. In model-based analysis we describe texture in terms of mathematical model which has both analysis and synthesis abilities. In the literature several comparative studies of feature-based methods are found. However few explicit comparative studies of model-based methods have been reported. This paper describes the development of some criteria to compare two model-based texture analysis methods (Time Series model and Markov Random Field model).