Rough-fuzzy set approach for color and texture based image segmentation

Abstract

Image segmentation is one of the most attractive problems in image processing. In image segmentation how to extract useful features from image has become crucial. However, color feature or texture feature, which are both wildly used features, could not process segmentation problem alone very well, especially when images are complex. We adopt a rough-fuzzy set approach, which can properly process high dimensionality, for image segmentation considering both color and texture features. This approach firstly constructs a structure named fuzzy data cube, whose attributes are composed of the fuzzy sets associated with image features. The fuzzy data cube, which can be two-dimension or high-dimension, is as the basic data structure in this method. A definition of the membership function of similarity relation based rough-fuzzy set is introduced as well as the definition of dependency function to evaluate the importance of an attribute for image segmentation. Then we used the rough-fuzzy set to discover the similarity set in fuzzy data cube to obtain the segmentation result. Experiments on mosaic and natural images are presented to demonstrate the effectiveness of the proposed method.