Classification and Retrieval of Images from Databases Using Rough Set Theory - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 179

Chapter x
Classification and Retrieval of
Images from Databases Using
Rough Set Theory
Aboul Ella Hassanien
Cairo University, Egypt
Jafar M. Ali
Kuwait University, Kuwait
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 dis-
tance 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.
introduction
The growth of the size of data and number of existing databases far exceeds the ability of humans to
analyze this data, which creates both a need and an opportunity to extract knowledge from databases.
There is a pressing need for efficient information management and mining of the huge quantities of
image data that are routinely being used in databases (Cios, Pedrycz, & Swiniarski, 1998; Laudon, &