International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 8- May 2015
Fuzzy Color Histogram Based Content
Based Image Retrieval of Query Images
Jaykrishna Joshi#1, Dattatray Bade*2, Kashyap Joshi#3
#1
Student, Department of Electronics & Telecommunication, ARMIET COE, Mumbai University, India
* Professor, Department of Electronics & Telecommunication, Vidhyalankar COE, Mumbai University, India.
#3
Professor, Department of Electronics & Telecommunication, MPSTME, NMIMS University, India
2
ABSTRACT
The need for efficient content-based image retrieval
system has increased hugely. Efficient and effective
retrieval techniques of images are desired because of
the explosive growth of digital images. Content based
image retrieval (CBIR) is a promising approach
because of its automatic indexing retrieval based on
their semantic features and visual appearance. In this
proposed system we use the the Fuzzy Color
Histogram(FCH) that uses a small number of bins
produced by linking the triplet from the L*a*b* color
space into a single histogram by means of a fuzzy
expert system. The a* and b*components are
considered to have more weight than L* as it is mostly
the combination of the two which provides the color
information of an image. One of the reasons why the
L*a*b* color space was selected is that it is a
perceptually uniform color space which approximates
the way that humans perceive color.
Keywords - Content based image retrieval (CBIR),
Fuzzy Color Histogram, Retrieval and
Query
Image.
that multimedia databases deal with text, audio, video
and image data which could provide enormous
amount of information and which has affected life
style of human for the better.
CBIR is the application of computer vision to the
image retrieval problem, that is, the problem of
searching for digital images in large databases[2].
Content-based image retrieval also known as query by
image content (QBIC) and content-based visual
information retrieval (CBVIR). "Content-based"
means that the search will analyze the actual contents
of the image. The term 'content' in this context might
refer colors, shapes, textures, or any other information
that can be derived from the image itself.
There are some types of feature used for Image
retrieval such as color retrieval, textual retrieval,
shape retrieval and so on. Figure 1 show diagram
fundamental of content-based image retrieval system.
I. INTRODUCTION
With the advances in computer technologies and
the advent of the World Wide Web, there has been an
explosion in the amount and complexity of digital
data being generated, stored, transmitted, analyzed,
and accessed. Much of this information is multimedia
in nature, including digital images, video, audio,
graphics, and text data[1]. In order to make use of this
vast amount of data, efficient and effective techniques
to retrieve multimedia information based on its
content need to be developed. Among the various
media types, images are of prime importance.
An image retrieval system is a computer system
for browsing, searching and retrieving images from a
large database of digital images. Most traditional and
common methods of image retrieval utilize some
method of adding metadata such as captioning,
keywords, or descriptions to the images so that
retrieval can be performed over the annotation words.
The reason behind research on multimedia systems
and content-based image retrieval (CBIR) is the fact
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Figure.1 Block Diagram of Basic Content Based Image
Retrieval System
II. CONTENT-BASED IMAGE
RETRIEVAL (CBIR)
Content-based image retrieval (CBIR) has
become one of the most active research areas in the
past few years. Thus, many visual feature
representations have been explored and many CBIR
systems have been built. However, there are several
problems and challenges need to be consider in
attempt to apply CBIR systems. Firstly, the gap
between high-level semantic concept and low-level
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 8- May 2015
visual features is great. In the CBIR context, an image
is represented by a set of low-level visual features
which are the features have no direct correlation with
high-level semantic concept[3]. Human prefer to
retrieve images according to the “semantic” or
“concept” of an image. But, CBIR depends on the
absolute distance of image features to retrieve similar
images Thereby, appear the gap between high-level
concepts and low-level features which is the major
difficulty that hinders further development of CBIR
systems. In other sentence, the semantic gap problem
is the lack of coincidence between the image
representation and the human interpretation for an
image.
There are many existing feature selection
techniques such as distribution based approaches,
Kullback-Leibler divergence (K-LD), boosting
manner, discriminant analysis (DA) method and
others. However, these feature selection techniques
remains a challenging problem for image retrieval. In
recent year, there are a lot of discriminant analysis
method had been proposed and used as a feature
selection method to improve relevance feedback.
These methods included multiple discriminant
analysis (MDA), biased discriminant analysis (BDA),
kernel-biased discriminant analysis (KBDA) and
nonparametric discriminant analysis (NDA). The goal
of discriminant analysis is to find a weight matrix
such that the distances between the two scatter class
matrixes are maximized.
However, these methods have their own
drawback that must be solved to improve the
performance of CBIR[4]. Basic single Gaussian
assumption which proposed by MDA and BDA
usually doesn‟t hold, since the few training samples
are always scattered in the high dimensional feature
space, and their effectiveness will be suffer.
Moreover, single Gaussian distribution means all
positive samples should be similar with similar view
angle and similar illumination, which are not the case
for CBIR. To overcome the problem of single
Gaussian distribution assumption, KBDA had been
introduced. But, this kernel based method has two
major drawbacks which is regularization approach is
often unstable and it is rely on parameter tuning.
Then, NDA had been proposed to solve the problem
in MDA, BDA and also KBDA. This approach can
only barely match the accuracy performance of
KBDA. As a conclusion, many feature selection
methods can not satisfy the requirements in CBIR
even though there are many method has been apply in
content-based image retrieval[5].
III. METHODOLOGY
The term Content-based image retrieval [CBIR]
describes the process of retrieving desired images
from a large collection on the basis of features (such
as colour, texture and shape) that can be automatically
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extracted from the images themselves.Content-based
image retrieval, also known as query by image content
and content-based visual information retrieval is the
application of computer vision to the image retrieval
problem, that is, the problem of searching for digital
images in large databases[6]. „CONTENT BASED‟
means that the search makes use of the contents of the
images themselves, rather than relying on humaninput metadata such as captions or keywords. The
surrounding world is composed of images.
There are some notable studies on fuzzy color
histogram and its application to content-based image
retrieval. Han an Ma introduce a fast approach for
computing fuzzy color histogram using fuzzy c-means
algorithm in They find a correspondence between
conventional color histogram (CCH) and fuzzy color
histogram (FCH), and compute theFCH of an image
without dealing with membership functions. It is
claimed that FCH improves robustness, efficiency,
and computation. Konstantinidis, Gasteratos and
Andreadis propose a fuzzy linking method for color
histogram creation in L*a*b* color space. They
evaluated their method on a limited dataset, and
achieve higheraccuracy values for several image
retrieval tests. Comparedto our work, fuzzy color
histogram of contains 9 bins.We extracted new colors
and find fuzzy rules. Our experiments also focus on
retrieving the original image, whereas they evaluate
the relevancy of retrieved images[7].
The classic histogram is a global statistical
feature, which describes the intensity distribution for a
given image. Its main advantage is that it is fast to
manipulate, store and compare and insensitive to
rotation and scale. On theother hand, it is also quite
unreliable as it is sensitive to even small changes in
the scene of the image. In color image processing, the
histogram consists of three components, respect to the
three components of the color space[8]. A histogram
is created by dividing a color space into a number of
bins and then by counting the number of pixels of the
image that belongs to each bin. It is usually thought
that in order for an image retrieval system to
performsatisfyingly, the number of regions that the
color space is divided into is quite large and thus the
colors represented by neighboring regions have
relatively small differences[9]. As a result, the
perceptually similar colors problem appears, images
which are similar to each other but have small
differences in scene or contain noise will produce
histograms with dissimilar adjacent bins and vice
versa due to the small distance that the regions are
separated from each other. In order to present a
solution to this problem , the FCH uses a small
number of bins produced by linking the triplet from
the L*a*b* color space into a single histogram by
means of a fuzzy expert system[7]. The a* and
b*components are considered to have more weight
than L* as it is mostly the combination of the two
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 8- May 2015
which provides the color information of an image.
IV. DESIGN STEPS
One of the reasons why the L*a*b* color space was
selected is that it is a perceptually uniform color space
which approximates the way that humans perceive
color. In L*a*b*, L* stands for luminance, a*
represents relative greenness-redness and b*
represents relative blueness-yellowness. All colorsand
grey levels can be expressed throughout a
combination of the three components. However, L*
does not contribute in providing any unique color but
for shades of colors, white, black and grey. Thus, the
L* component receives a lower weight with respect to
the other two components of the triplet. According to
the work mentioned in [7] ,in order for the CBIR
system to work effectively the a* and b* components
should be subdivided intofive regions representing
green, greenish, the middle of the component, reddish
and red for a*, blue, bluish, the middle of the
component, yellowish and yellow for b*, whereas L*
should be subdivided into only three regions: dark
dim and bright areas. The Fuzzification of the input is
accomplished by using triangular shaped builtinmembership functions (MF) for the three input
components (L*, a*, b*) as shown in fig .2. The
Mamdani type of fuzzy inference is used in which the
fuzzy sets from the output MFs of each rule are
combined through the aggregation operator which is
set to max and the resulting fuzzy set is defuzzified to
produce the output of the system. The implication
factor which determines the process of shaping the
fuzzy set in the output Membership Functions(MFs)
based on the results of the input MFs is set to min and
the OR and AND operators are set to max and min,
respectively.
The followinf Figure 2 shows the fuzzy color
histogram process.
This process is divided five steps:
Step-1: RGB to L*a*b* conversion:
Convert RGB color space image components
(pixel values) to L*a*b* color components called fuzzy
sets.On sorting out the top 5 images afterfeature
extraction fuzzy logic is applied to these images.RGB
Color space is converted to first L*a*b* color
space.L*a*b* is commonly preferred over RGB or
other colorspaces, because it is one of the perceptually
uniform color spaces which approximates the way that
human perceivecolor. In L*a*b* color space, L*
represents luminance, a*represent greenness-redness
and b* represents bluenessyellowness. a* and b*
components have more weights thanL*component.
Step-2: Fuzzification:
Fuzzification of each input pixel into l*a*b*
using triangular membership function ’trimf’. Take the
inputs and determine the degree to which these inputs
belong to each of the appropriate fuzzy sets.
Membership functions are shown in Figure 3,4 and 5.
L* is divided into 3 regions which are Black, Gray and
White.
Figure 3. Triangular membership function for L*
a* is dividedinto 5 regions which are and red,
reddish,a_middle, greenish and green
Figure 2 Structure of fuzzy color histogram
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 8- May 2015
Figure 4. Triangular membership function for a*
B* is divided into 5 regions which are Blue, Bluish,
b_middle, Yellowish, Yellow
Figure 5. Triangular membership function for b*
Step-3: Aggregation:
Then the fuzzy rules are evaluated and their
outputs are combined by MAX aggregation operator.
This is done to change the aggregate output fuzzy set
into a single crisp number. Aggregation is the process
of unification of the outputs of all rules. We take the
membership functions of all rule consequents
previously clipped or scaled and combine them into a
single fuzzy set. The input of the aggregation process
is the list of clipped or scaled consequent membership
functions, and the output is one fuzzy set for each
output variable.
Table 1. Some of the Fuzzy Rules
Rule
No.
L*Colors
a*
colors
b*colors
Fuzzy
colors
V. RESULTS
The process involved in the proposed method is
Fuzzy Histogram Error calculation. The Fuzzy
histogram is applied only to the top five extracted
images to get better retrieval accuracy. After plotting
the fuzzy histogram, the fuzzy error is calculated
between the given query image and the top five
extracted images which is shown in the table.(3) below.
Fuzzy error is calculated as:
1
Black
Red
Blue
Black
2
3
Black
Gray
Red
Red
Yellow
Blue
Blue
Red
4
Grey
Green
Blue
Yellow
5
White
Red
Yellowish
6
White
Red
Blue
Dark
Pink
Red
Step-4: Defuzzification:
The process of the deffuzification is to change
the aggregate output fuzzyset in crisp number. It
involvesfuzzy linking using fuzzy rules. After
Fuzzification, each fuzzified output (l*a*b* values) of
pixel is verified under 75 fuzzy color rules and a
single resultant fuzzy color is obtained for a pixel i.e.
these components are then linked using seventy five
fuzzy rules obtained by using all possible
combinations of L*, a* and b* i.e.the 75 rules are
created using 3 L*, 5 a* and 5 b* color space
combinations (3×5×5=75). The following table
summarizes some colors and their fuzzy
correspondences.Because some colors (olive, purple,
dark pink, lime, bluish- green) reside very close to
others in 3-DL*a*b* space, we selected the remaining
9 colors. Therefore, the final fuzzy color histogram
contains 9 bins only.
Step-5: Calculation of fuzzy histogram and
histogram error:
Fuzzy histogram is then calculated wherein the
pixel is approximately put in the corresponding color
bin. After this we calculate the fuzzy histogram error
between the Query image and the top 5 sorted images.
image. From the table 2 it should be noted that the fuzzy
error calculated for Pepper_1.png which is rotated
version of query image is 0.0025 which is
approximately close to query image. Still it is not the
perfect match and hence not the retrieved image. Then
table 5 also shows that the fuzzy error for fabric.png is
0.1150 which signifies that it is not at all a match to the
query image Peppers.png. This shows the accuracy of
the proposed Fuzzy system.A higher successful rate in
retrieving a target image is thus obtained.
=
The Fuzzy error for Peppers.png is zero which is
perfect match to the given query image. Hence finally
obtain Peppers.png as the retrieved version of query
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 8- May 2015
Table.2 Fuzzy Histogram Error
Sr no
Image name
Fuzzy error
1
Peppers.png
0
2
Pepper_1.png
0.0025
3
Pepper_2.png
0.0100
4
Pepper_3.png
0.0087
5
Fabric.png
0.1150
which use of gradients in different channels that
weight the influence of a pixel on the histogram to
cancel out the changes induced by deformations.
When a rotated image is given as the query, the
original image is retrieved as the closest match as
shown in the results. To reduce the large variations
between neighboring bins of conventional color
histograms, fuzzy color histograms are adopted
which consists of only nine bins. It is less sensitive to
various changes in the images such lightning
variations and noise.
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Figure 6. Query Image and Retrieved Image
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The retrieved image is “peppers.png” as shown
in Figure 6.
VI. CONCLUSION
The conventional color histogram with Euclidean
Distance as similarity measure and the fuzzy color
histogram are almost similar in their performance.
But the latter couldn‟t respond well to shifted or
translated images. In order to overcome this problem
invariant color histogram technique is used makes
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