Classification and Retrieval of Images from Databases Using Rough Set Theory

Abstract

This chapter presents an efficient algorithm to classify and retrieve images from large databases in the context of rough set theory. Color and texture are two well-known low-level perceptible features to describe an image contents used in this chapter. The features are extracted, normalized, and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. We test three different popular distance measures in this work and find that quadratic distance measures provide the most accurate and perceptually relevant retrievals. The retrieval performance is measured using recall-precision measure, as is standard in all retrieval systems.