@ARTICLE { Rsds1955,
author = {Hassanien, Aboul}, 
title = {Image classification and retrieval algorithm based on rough set theory}, 
journal = {South African Computer Journal (SACJ)}, 
volume = 30, 
pages = {9-16}, 
month = {July}, 
year = 2003, 
abstract = {This paper 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 paper. 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.}, 
keywords = {rough set theory (RST), classification, feature extraction, rule generation, similarity measure, rule reduction, }, 
}