Classification and Retrieval of Images from Databases Using Rough Set Theory - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 180

Laudon, 2006; Starzyk, Dale, & Sturtz, 2000). These data are potentially an extremely valuable source
of information, but their value is limited unless they can be effectively explored and retrieved, and it
is becoming increasingly clear that in order to be efficient, data mining must be based on Semantics.
However, the extraction of Semantically rich meta-data from computationally accessible low-level fea-
tures poses tremendous scientific challenges (Laudon & Laudon, 2006; Mehta, Agrawal, & Rissanen,
1996; Mitra, Pal, & Mitra, 2002).
Content-based image classify and retrieval (CBICR) systems are needed to effectively and efficiently
use the information that is intrinsically stored in these image databases. This image retrieval system
has gained considerable attention, especially during the last decade. Image retrieval based on content is
extremely useful in many applications (Smith, 1998; Molinier, Laaksonen, Ahola, & Häme, 2005; Yang
& Laaksonen, 2005; Koskela, Laaksonen, & Oja, 2004; Viitaniemi & Laaksonen, 2006; Huang, Tan,
& Loew, 2003; Smeulders, Worring, Santini, Gupta., & Jain, 2000; Ma & Manjunath, 1999; Carson,
Thomas, Belongie, Hellerstein, & Malik, 1999) such as crime prevention, the military, intellectual
property, architectural and engineering design, fashion and interior design, journalism and advertising,
medical diagnosis, geographic information and remote sensing systems, cultural heritage, education
and training, home entertainment, and Web searching. In a typical CBIR system, quires are normally
formulated either by query by example or similarity retrieval, selecting from a color, shape, skelton, and
texture features or a combination of two or more features. The system then compares the query with
a database representing the stored images. The output from a CBIR system is usually a ranked list of
images in order of their similarity to the query.
Image classification (Hassanien & Dominik 2007) is an important data mining task which can be
defined as a task of finding a function that maps items into one of several discrete classes. The most
commonly used techniques in classification are neural network [Dominik et. al. 2004, Hassanien &
Dominik 2007], genetic algorithms [Satchidananda et. al., 2008], decision trees [Yang et. al., 2003],
fuzzy theory [Ashish G., Saroj K. Meher, & Uma B. Shankar 2008], multi-resolution wavelet [Uma et.
al., 2007] and rough set theory [Hassanien & Ali, 2004]. Rough set concept was introduced by Polish
logician, Professor Zdzisław Pawlak in early eighties [Pawlak, Z. 1982]. This theory become very popu-
lar among scientists around the world and the rough set is now one of the most developing intelligent
data analysis [Slowinski, 1995, Pawlak, 1995, Pawlak, 1991]. Rough sets data analysis was used for
the discovery of data dependencies, data reduction, approximate set classification, and rule induction
from databases. The generated rules represent the underlying Semantic content of the images in the
database. A classification mechanism is developed by which the images are classified according to the
generated rules.
Image searching (Graham, 2004) is one of the most important services that need to be supported by
such systems. In general, two different approaches have been applied to allow searching on image col-
lections: one based on image textual metadata and another based on image content information. The first
retrieval approach is based on attaching textual metadata to each image and uses traditional database
query techniques to retrieve them by keyword. However, these systems require a previous annotation
of the database images, which is a very laborious and time-consuming task. Furthermore, the annota-
tion process is usually inefficient because users, generally, do not make the annotation in a systematic
way. In fact, different users tend to use different words to describe the same image characteristic. The
lack of systematization in the annotation process decreases the performance of the keyword-based im-
age search. These shortcomings have been addressed by so-called content-based image classification
and retrieval . In CBICR systems, image processing algorithms are used to extract feature vectors that