14-12-2012, 03:06 PM
A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm
[attachment=43772]
Abstract
Digital image libraries and other multimedia databases
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 interactive
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.
INTRODUCTION
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 contentbased,
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.
RELATED WORKS
There are some literatures that survey the most important
CBIR systems [7], [8]. Also, there are some papers that
overview and compare the current techniques in this area
[9], [10]. Since the early studies on CBIR, various color
descriptors have been adopted. Yoo et al. [11] proposed a
signature-based color-spatial image retrieval system. Color and
its spatial distribution within the image are used for the features.
In [12], a CBIR scheme based on the global and local color
distributions in an image is presented. Vadivel et al. [13] have
introduced an integrated approach for capturing spatial variation
of both color and intensity levels and shown its usefulness
in image retrieval applications.
Texture is also an essential visual feature in defining highlevel
semantics for image retrieval purposes. In [14], a novel,
effective, and efficient characterization of wavelet subbands by
bit-plane extractions in texture image retrieval was presented. In
order to overcome some limitations, such as computational expensive
approaches or poor retrieval accuracy, in a few texturebased
image retrieval methods, Kokare et al. [15] concentrated
on the problem of finding good texture features for CBIR.
They designed 2-D rotated complex wavelet filters to efficiently
handle texture images and formulate a new texture-retrieval
algorithm using the proposed filters.
Texture Descriptor
Texture is an important attribute that refers to innate surface
properties of an object and their relationship to the surrounding
environment. If we could choose appropriate texture descriptors,
the performance of the CBIR should be improved. We
use a gray level co-occurrence matrix (GLCM), which is a
simple and effective method for representing texture [26]. The
GLCM represents the probability p(i, j; d, θ) that two pixels in
an image, which are located with distance d and angle θ, have
gray levels i and j.
PROPOSED SYSTEM
In general, an image retrieval system usually provides a
user interface for communicating with the user. It collects the
required information, including the query image, from the user
and displays the retrieval results to him. However, as the images
are matched based on low-level visual features, the target or
the similar images may be far away from the query in the
feature space, and they are not returned in the limited number
of retrieved images of the first display. Therefore, in some
retrieval systems, there is a relevance feedback from the user,
where human and computer can interact to increase retrieval
performance.
EXPERIMENTAL RESULTS
To show the effectiveness of the proposed system, some experiments
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 experiments.
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.
CONCLUSION
This paper has presented a user-oriented framework in interactive
CBIR system. In contrast to conventional approaches that
are based on visual features, our method provides an interactive
mechanism to bridge the gap between the visual features and
the human perception. The color distributions, the mean value,
the standard deviation, and image bitmap are used as color
information of an image. In addition, the entropy based on the
GLCM and edge histogram are considered as texture descriptors
to help characterize the images. In particular, the IGA can
be considered and used as a semiautomated exploration tool
with the help of a user that can navigate a complex universe
of images.
[attachment=43772]
Abstract
Digital image libraries and other multimedia databases
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 interactive
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.
INTRODUCTION
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 contentbased,
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.
RELATED WORKS
There are some literatures that survey the most important
CBIR systems [7], [8]. Also, there are some papers that
overview and compare the current techniques in this area
[9], [10]. Since the early studies on CBIR, various color
descriptors have been adopted. Yoo et al. [11] proposed a
signature-based color-spatial image retrieval system. Color and
its spatial distribution within the image are used for the features.
In [12], a CBIR scheme based on the global and local color
distributions in an image is presented. Vadivel et al. [13] have
introduced an integrated approach for capturing spatial variation
of both color and intensity levels and shown its usefulness
in image retrieval applications.
Texture is also an essential visual feature in defining highlevel
semantics for image retrieval purposes. In [14], a novel,
effective, and efficient characterization of wavelet subbands by
bit-plane extractions in texture image retrieval was presented. In
order to overcome some limitations, such as computational expensive
approaches or poor retrieval accuracy, in a few texturebased
image retrieval methods, Kokare et al. [15] concentrated
on the problem of finding good texture features for CBIR.
They designed 2-D rotated complex wavelet filters to efficiently
handle texture images and formulate a new texture-retrieval
algorithm using the proposed filters.
Texture Descriptor
Texture is an important attribute that refers to innate surface
properties of an object and their relationship to the surrounding
environment. If we could choose appropriate texture descriptors,
the performance of the CBIR should be improved. We
use a gray level co-occurrence matrix (GLCM), which is a
simple and effective method for representing texture [26]. The
GLCM represents the probability p(i, j; d, θ) that two pixels in
an image, which are located with distance d and angle θ, have
gray levels i and j.
PROPOSED SYSTEM
In general, an image retrieval system usually provides a
user interface for communicating with the user. It collects the
required information, including the query image, from the user
and displays the retrieval results to him. However, as the images
are matched based on low-level visual features, the target or
the similar images may be far away from the query in the
feature space, and they are not returned in the limited number
of retrieved images of the first display. Therefore, in some
retrieval systems, there is a relevance feedback from the user,
where human and computer can interact to increase retrieval
performance.
EXPERIMENTAL RESULTS
To show the effectiveness of the proposed system, some experiments
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 experiments.
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.
CONCLUSION
This paper has presented a user-oriented framework in interactive
CBIR system. In contrast to conventional approaches that
are based on visual features, our method provides an interactive
mechanism to bridge the gap between the visual features and
the human perception. The color distributions, the mean value,
the standard deviation, and image bitmap are used as color
information of an image. In addition, the entropy based on the
GLCM and edge histogram are considered as texture descriptors
to help characterize the images. In particular, the IGA can
be considered and used as a semiautomated exploration tool
with the help of a user that can navigate a complex universe
of images.