12-03-2014, 04:25 PM
A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm
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Abstract
Digital image libraries and other multimedia data-
bases have been dramatically expanded in recent years. In order
to effectively and precisely retrieve the desired images from a
large image database, the development of a content-based image
retrieval (CBIR) system has become an important research issue.
However, most of the proposed approaches emphasize on finding
the best representation for different image features. Furthermore,
very few of the representative works well consider the user’s
subjectivity and preferences in the retrieval process. In this paper,
a user-oriented mechanism for CBIR method based on an inter-
active genetic algorithm (IGA) is proposed. Color attributes like
the mean value, the standard deviation, and the image bitmap of
a color image are used as the features for retrieval. In addition,
the entropy based on the gray level co-occurrence matrix and the
edge histogram of an image are also considered as the texture
features. Furthermore, to reduce the gap between the retrieval
results and the users’ expectation, the IGA is employed to help the
users identify the images that are most satisfied to the users’ need.
Experimental results and comparisons demonstrate the feasibility
of the proposed approach.
I NTRODUCTION
IN RECENT years, rapid advances in science and technology
have produced a large amount of image data in diverse areas,
such as entertainment, art galleries, fashion design, education,
medicine, industry, etc. We often need to efficiently store and
retrieve image data to perform assigned tasks and to make a
decision. Therefore, developing proper tools for the retrieval
image from large image collections is challenging.
Two different types of approaches, i.e., text- and content-
based, are usually adopted in image retrieval. In the text-based
system, the images are manually annotated by text descriptors
and then used by a database management system to perform
image retrieval. However, there are two limitations of using
keywords to achieve image retrieval: the vast amount of labor
required in manual image annotation and the task of describing
image content is highly subjective.
E XPERIMENTAL R ESULTS
To show the effectiveness of the proposed system, some ex-
periments will be reported. Selecting a suitable image database
is a critical and important step in designing an image retrieval
system. At the present time, there is not a standard image
database for this purpose. Also, there is no agreement on the
type and the number of images in the database. Since most
image retrieval systems are intended for general databases, it
is reasonable to include various semantic groups of images in
the database. In our experiments, we used the database of the
SIMPLIcity project [41] covering a wide range of semantic
categories from natural scenes to artificial objects for experi-
ments. The database is partitioned into ten categories, including
African people and village, beach, buildings, buses, dinosaurs,
elephants, flowers, horses, mountains and glaciers, food, etc.,
and each category contains 100 images (Fig. 2). Partitioning
of the database into semantic categories is determined by the
creators and reflects the human perception of image similarity.