Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Manimala Singha* and K.Hemachandran$
Dept. of Computer Science, Assam University, Silchar
India. Pin code 788011
*n.manimala888@gmail.com,$khchandran@rediffmail.com
ABSTRACT
The increased need of content based image retrieval technique can be found in a number of different
domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting,
Remote Sensing and Management of Earth Resources. This paper presents the content based image
retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image
Retrieval).The texture and color features are extracted through wavelet transformation and color
histogram and the combination of these features is robust to scaling and translation of objects in an
image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image
database containing 1000 general-purpose color images. The performance has been evaluated by
comparing with the existing systems in the literature.
Keywords
Image Retrieval, Color Histogram, Color Spaces, Quantization, Similarity Matching, Haar Wavelet,
Precision and Recall.
1. INTRODUCTION
Research on content-based image retrieval has gained tremendous momentum during the last
decade. A lot of research work has been carried out on Image Retrieval by many researchers,
expanding in both depth and breadth [1]-[5]. The term Content Based Image Retrieval (CBIR)
seems to have originated with the work of Kato [6] for the automatic retrieval of the images from
a database, based on the color and shape present. Since then, the term has widely been used to
describe the process of retrieving desired images from a large collection of database, on the basis
of syntactical image features (color, texture and shape). The techniques, tools and algorithms that
are used, originate from the fields, such as statistics, pattern recognition, signal processing, data
mining and computer vision. In the past decade, many image retrieval systems have been
successfully developed, such as the IBM QBIC System [7], developed at the IBM Almaden
Research Center, the VIRAGE System [8], developed by the Virage Incorporation, the Photobook
System [9], developed by the MIT Media Lab, the VisualSeek System [10], developed at
Columbia University, the WBIIS System [11] developed at Stanford University, and the
Blobworld System [12], developed at U.C. Berkeley and SIMPLIcity System [13]. Since simply
color, texture and shape features cannot sufficiently represent image semantics, semantic-based
image retrieval is still an open problem. CBIR is the most important and effective image retrieval
method and widely studied in both academia and industry arena. In this paper we propose an
image retrieval system, called Wavelet-Based Color Histogram Image Retrieval (WBCHIR),
DOI : 10.5121/sipij.2012.3104
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
based on the combination of color and texture features.The color histogram for color feature and
wavelet representation for texture and location information of an image. This reduces the
processing time for retrieval of an image with more promising representatives. The extraction of
color features from digital images depends on an understanding of the theory of color and the
representation of color in digital images. Color spaces are an important component for relating
color to its representation in digital form. The transformations between different color spaces and
the quantization of color information are primary determinants of a given feature extraction
method. Color is usually represented by color histogram, color correlogram, color coherence
vector and color moment, under certain a color space [14-17]. The color histogram feature has
been used by many researchers for image retrieval [18 and 19]. A color histogram is a vector,
where each element represents the number of pixels falling in a bin, of an image [20]. The color
histogram has been used as one of the feature extraction attributes with the advantage like
robustness with respect to geometric changes of the objects in the image. However the color
histogram may fail when the texture feature is dominant in an image [21]. Li and Lee [22] have
proposed a ring based fuzzy histogram feature to overcome the limitation of conventional color
histogram. The distance formula used by many researchers, for image retrieval, include
Histogram Euclidean Distance, Histogram Intersection Distance, Histogram Manhattan
Distance and Histogram Quadratic Distance [23-27].
Texture is also considered as one of the feature extraction attributes by many researchers [28-31].
Although there is no formal definition for texture, intuitively this descriptor provides measures of
the properties such as smoothness, coarseness, and regularity. Mainly the texture features of an
image are analyzed through statistical, structural and spectral methods [32].
The rest of the paper is organized as follows: In section 2, a brief review of the related work is
presented. The section 3 describes the color feature extraction. The section 4, presents the texture
feature extraction and the section 5, presents the similarity matching. The proposed method is
given in section 6 and section 7 describes the performance evaluation of the proposed method.
Finally the experimental work and the conclusions are presented in section 8 and section 9
respectively.
2. RELATED WORK
Lin et al. [14] proposed a color-texture and color-histogram based image retrieval system
(CTCHIR). They proposed (1) three image features, based on color, texture and color
distribution, as color co-occurrence matrix (CCM), difference between pixels of scan pattern
(DBPSP) and color histogram for K-mean (CHKM) respectively and (2) a method for image
retrieval by integrating CCM, DBPSP and CHKM to enhance image detection rate and simplify
computation of image retrieval. From the experimental results they found that, their proposed
method outperforms the Jhanwar et al. [33] and Hung and Dai [34] methods. Raghupathi et al.
[35] have made a comparative study on image retrieval techniques, using different feature
extraction methods like color histogram, Gabor Transform, color histogram+gabour transform,
Contourlet Transform and color histogram+contourlet transform. Hiremath and Pujari [36]
proposed CBIR system based on the color, texture and shape features by partitioning the image
into tiles. The features computed on tiles serve as local descriptors of color and texture features.
The color and texture analysis are analyzed by using two level grid frameworks and the shape
feature is used by using Gradient Vector Flow. The comparison of experimental result of
proposed method with other system [37]-[40] found that, their proposed retrieval system gives
better performance than the others. Rao et al. [41] proposed CTDCIRS (color-texture and
dominant color based image retrieval system), they integrated three features like Motif cooccurrence matrix (MCM) and difference between pixels of scan pattern (DBPSP) which
describes the texture features and dynamic dominant color (DDC) to extract color feature. They
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
compared their results with the work of Jhanwar et al. [33] and Hung and Dai [34] and found that
their method gives better retrieval results than others.
3. COLOR FEATURE
The color feature has widely been used in CBIR systems, because of its easy and fast
computation [42]-[43]. Color is also an intuitive feature and plays an important role in image
matching. The extraction of color features from digital images depends on an understanding of
the theory of color and the representation of color in digital images. The color histogram is one of
the most commonly used color feature representation in image retrieval. The original idea to use
histogram for retrieval comes from Swain and Ballard [27], who realized the power to identify an
object using color is much larger than that of a gray scale.
3.1 COLOR SPACE SELECTION AND COLOR QUANTIZATION
The color of an image is represented, through any of the popular color spaces like RGB, XYZ,
YIQ, L*a*b*, U*V*W*, YUV and HSV [44]. It has been reported that the HSV color space gives
the best color histogram feature, among the different color spaces [45]-[49]. The application of
the HSV color space in the content-based image retrieval has been reported by Surel et al. [50]. In
HSV color space the color is presented in terms of three components: Hue (H), Saturation (S) and
Value (V) and the HSV color space is based on cylinder coordinates [51 and 52].
Color quantization is a process that optimizes the use of distinct colors in an image without
affecting the visual properties of an image. For a true color image, the distinct number of colors is
up to 224 = 16777216 and the direct extraction of color feature from the true color will lead to a
large computation. In order to reduce the computation, the color quantization can be used to
represent the image, without a significant reduction in image quality, thereby reducing the storage
space and enhancing the process speed [53]. The effect of color quantization on the performance
of image retrieval has been reported by many authors [53 and 54].
3.2 Color Histogram
A color histogram represents the distribution of colors in an image, through a set of bins, where
each histogram bin corresponds to a color in the quantized color space. A color histogram for a
given image is represented by a vector:
H = H 0 ,H 1 ,H 2 ,H 3 ,…………H i ,………,H n
Where i is the color bin in the color histogram and H[i] represents the number of pixels of color i
in the image, and n is the total number of bins used in color histogram. Typically, each pixel in an
image will be assigned to a bin of a color histogram. Accordingly in the color histogram of an
image, the value of each bin gives the number of pixels that has the same corresponding color. In
order to compare images of different sizes, color histograms should be normalized. The
normalized color histogram H is given as:
Where
H i =
H = H 0 ,H 1 ,H 2 ,……,H i ,….,H n
, p is the total number of pixels of an image [55].
4. TEXTURE FEATURE
Like color, the texture is a powerful low-level feature for image search and retrieval applications.
Much work has been done on texture analysis, classification, and segmentation for the last four
decade, still there is a lot of potential for the research. So far, there is no unique definition for
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
texture; however, an encapsulating scientific definition as given in [56] can be stated as, “Texture
is an attribute representing the spatial arrangement of the grey levels of the pixels in a region or
image”. The common known texture descriptors are Wavelet Transform [57], Gabor-filter [58],
co-occurrence matrices [59] and Tamura features [60]. We have used Wavelet Transform, which
decomposes an image into orthogonal components, because of its better localization and
computationally inexpensive properties [30 and 31].
4.1 Haar Discrete Wavelet Transforms
Discrete wavelet transformation (DWT) [61] is used to transform an image from spatial domain
into frequency domain. The wavelet transform represents a function as a superposition of a family
of basis functions called wavelets. Wavelet transforms extract information from signal at different
scales by passing the signal through low pass and high pass filters. Wavelets provide multiresolution capability and good energy compaction. Wavelets are robust with respect to color
intensity shifts and can capture both texture and shape information efficiently. The wavelet
transforms can be computed linearly with time and thus allowing for very fast algorithms [28].
DWT decomposes a signal into a set of Basis Functions and Wavelet Functions. The wavelet
transform computation of a two-dimensional image is also a multi-resolution approach, which
applies recursive filtering and sub-sampling. At each level (scale), the image is decomposed into
four frequency sub-bands, LL, LH, HL, and HH where L denotes low frequency and H denotes
high frequency as shown in Figure1.
Figure 1. Discrete Wavelet Sub-band Decomposition
Haar wavelets are widely being used since its invention after by Haar [62]. Haar used these
functions to give an example of a countable orthonormal system for the space of squareintegrable functions on the real line. In this paper, we have used Haar wavelets to compute feature
signatures, because they are the fastest to compute and also have been found to perform well in
practice [63]. Haar wavelets enable us to speed up the wavelet computation phase for thousands
of sliding windows of varying sizes in an image. The Haar wavelet's mother wavelet function (t)
can be described as:
t =
1 ,0 ≤ t ≤
1
2
%
1
−1 , ≤ t < 1
2
0 , otherwise
1
and its scaling function (t) can be described as:
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
t = &
1 ,0 ≤ t < 1 %
0 , otherwise
2
5. FEATURE SIMILARITY MATCHING
The Similarity matching is the process of approximating a solution, based on the computation of a
similarity function between a pair of images, and the result is a set of likely values. Exactness,
however, is a precise concept. Many researchers have used different similarity matching
techniques [23]-[27]. In our study, we have used the Histogram Intersection Distance method, for
the reasons given in [64].
5.1 Histogram Intersection Distance:
Swain and Ballard [27] proposed histogram intersection for color image retrieval. Intersection of
histograms was originally defined as:
d() =
* 01
02 min,Q i , D i /
|D i |
3
Smith and Chang [55] extended the idea, by modifying the denominator of the original definition,
to include the case when the cardinalities of the two histograms are different and expressed as:
d()
* 01
02 min Q i , D i
min |Q i |, D i
4
and |Q| and |D| represents the magnitude of the histogram for query image and a representative
image in the Database.
6. PROPOSED METHOD
In this study we are proposing two algorithms for image retrieval based on the color histogram
and Wavelet-based Color Histogram. The block diagrams of the proposed methods are shown in
Figure 2. and Figure 3.
6.1. Color Histogram
Step 1. Convert RGB color space image into HSV color space.
Step 2. Color quantization is carried out using color histogram by assigning 8 level each to
hue, saturation and value to give a quantized HSV space with 8x8x8=512 histogram
bins.
Step 3. The normalized histogram is obtained by dividing with the total number of pixels.
Step 4. Repeat step1 to step3 on an image in the database.
Step 5. Calculate the similarity matrix of query image and the image present in the database.
Step 6. Repeat the steps from 4 to 5 for all the images in the database.
Step 7. Retrieve the images.
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Query Image
Image Database
Convert RGB into HSV
Convert RGB into HSV
Quantize HSV: (8, 8, 8)
Quantize HSV: (8, 8, 8)
Compute the Histogram
Compute the Histogram
Similarity computation
with distance function
Retrieved Images
Figure 2. Block diagram of proposed Color Histogram
6.2. Wavelet-Based Color Histogram (WBCH).
Step1. Extract the Red, Green, and Blue Components from an image.
Step2. Decompose each Red, Green, Blue Component using Haar Wavelet transformation at 1st
level to get approximate coefficient and vertical, horizontal and diagonal detail
coefficients.
Step3. Combine approximate coefficient of Red, Green, and Blue Component.
Step4. Similarly combine the horizontal and vertical coefficients of Red, Green, and Blue
Component.
Step5. Assign the weights 0.003 to approximate coefficients, 0.001 to horizontal and 0.001 to
vertical coefficients (experimentally observed values).
Step6. Convert the approximate, horizontal and vertical coefficients into HSV plane.
Step7. Color quantization is carried out using color histogram by assigning 8 level each to hue,
saturation and value to give a quantized HSV space with 8x8x8=512 histogram bins.
Step8. The normalized histogram is obtained by dividing with the total number of pixels.
Step9. Repeat step1 to step8 on an image in the database.
Step10. Calculate the similarity matrix of query image and the image present in the database.
Step11. Repeat the steps from 9 to 10 for all the images in the database.
Step12. Retrieve the images.
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Query Image
Image Database
Image Decomposition using
(Haar Wavelets)
Image Decomposition using
(Haar Wavelets)
Convert (A, H and V) of RGB
into HSV
Convert (A, H and V) of RGB
into HSV
Quantize HSV: (8, 8, 8)
Quantize HSV: (8, 8, 8)
Compute the Histogram
Compute the Histogram
Similarity matrix
computation
Retrieved Images
Figure 3. Block diagram of proposed Wavelet-Based Color Histogram (WBCH). (A-approximate
coefficient, H-horizontal detail coefficient, V-vertical detail coefficient).
7. PERFORMANCE EVALUATION
The performance of retrieval of the system can be measured in terms of its recall and precision.
Recall measures the ability of the system to retrieve all the models that are relevant, while
precision measures the ability of the system to retrieve only the models that are relevant. It has
been reported that the histogram gives the best performance through recall and precision value
[35, 44]. They are defined as:
Precision =
Recall =
789:;< => <;?;@A1B 9AC;D <;B< ;@;E
F=BA? 189:;< => 9AC;D <;B< ;@;E
789:;< => <;?;@A1B 9AC;D <;B< ;@;E
F=BA? 189:;< => <;?;@A1B 9AC;D
= GHI
G
= GHK
G
(5)
(6)
Where A represent the number of relevant images that are retrieved, B, the number of irrelevant
items and the C, number of relevant items those were not retrieved. The number of relevant items
retrieved is the number of the returned images that are similar to the query image in this case. The
total number of items retrieved is the number of images that are returned by the search engine.
The average precision for the images that belongs to the qth category (Aq) has been computed by
[65]
L = M
O∈TU
L NO
PQR P
Where q=1, 2……10.
Finally, the average precision is given by:
L = *2X
R02 LR /10.
7
8
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
8. EXPERIMENT
The proposed method has been implemented using Matlab 7.3 and tested on a general-purpose
WANG database [66] containing 1,000 images of the Corel stock photo, in JPEG format of size
384x256 and 256x386 as shown in Figure 4. The search is usually based on similarity rather than
the exact match. We have followed the image retrieval technique, as described in the section 6.1
on different quantization schemes. The quality of the image retrieval, with different quantization
schemes like (4, 4, 4), (4, 8, 8), (8, 4, 4), (8, 8, 4), (8, 8, 8), (16, 4, 4) and (18, 3, 3) has been
evaluated by randomly selecting 10 query images, of each category, from the image database.
Each query returns the top 10 images from database, and the calculated precision values, using
the equation 5, and average precision using equation 8 are given in the Table 1. The average
precision (7.8) value of (8, 8, 8) quantization bin indicates the better retrieval results than the
others.
Table 1. Precision Using Different Quantization Schemes
Category
African
People
4,4,4
4,4,8
4,8,8
8,4,4
8,8,4
8,8,8
16,4,4
18,3,3
9
9
9
9
9
9
10
9
Beach
6
5
5
4
6
6
3
5
Building
6
6
6
6
7
6
8
9
Buses
9
9
9
9
9
9
9
8
Dinosaurs
8
10
9
7
8
9
8
8
Elephants
7
8
8
7
8
9
7
7
Flowers
6
6
6
7
7
7
7
6
Horses
9
9
9
10
9
9
10
10
Mountains
6
6
6
5
5
6
6
5
Food
8
7
9
8
8
8
8
8
Average
precision
7.4
7.5
7.6
7.2
7.6
7.8
7.6
7.5
The WBCH method, as discussed in section 6.2, has been used to study the image retrieval using
(8,8,8) color quantization bin and the performance of the proposed image retrieval technique has
been evaluated by comparing the results with the results of different authors [14, 35, 36 and 41]
as shown in the Table 2. The effectiveness of the WBCH retrieval method is evaluated by
selecting 10 query images under each category of different semantics. For each query, we
examined the precision of the retrieval, based on the relevance of the image semantics. The
semantic relevance is determined by manual truthing the query image and each of the retrieved
images in the retrieval. The precision values, calculated by using the equation 5 and also the
average precision using the equation 8 are shown in Table 2. The 10 query retrievals by the
proposed method are shown in Figures 5-14, with an average retrieval time as 1min. These results
clearly show that the performance of the proposed method is better than the other methods.
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Table 2: Precision of the Retrieval by different methods
Classes
Category
WBCH
CH
[14]
[35]
[36]
[41]
1
African people
0.65
0.72
0.68
0.75
0.54
0.562
2
Beach
0.62
0.53
0.54
0.6
0.38
0.536
3
Building
0.71
0.61
0.56
0.43
0.30
0.61
4
Buses
0.92
0.93
0.89
0.69
0.64
0.893
5
Dinosaurs
0.97
0.95
0.99
1
0.96
0.984
6
Elephants
0.86
0.84
0.66
0.72
0.62
0.578
7
Flowers
0.76
0.66
0.89
0.93
0.68
0.899
8
Horses
0.87
0.89
0.8
0.91
0.75
0.78
9
Mountains
0.49
0.47
0.52
0.36
0.45
0.512
10
Food
0.77
0.82
0.73
0.65
0.53
0.694
Average Precision
0.762
0.742
0.726
0.704
0.585
0.7048
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 4. Sample of WANG Image Database
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 5. Retrieve results for African People
Figure 6. Retrieve results for Beach
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 7. Retrieve results for Building
Figure 8. Retrieve results for Bus
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 9. Retrieve results for Dinosaurs
Figure 10. Retrieve results for Elephants
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 11. Retrieve results for Flowers
Figure 12. Retrieve results for Horses
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Figure 13. Retrieve results for Mountains
Figure 14. Retrieve results for Food
9. CONCLUSION
In this paper, we presented a novel approach for Content Based Image Retrieval by combining the
color and texture features called Wavelet-Based Color Histogram Image Retrieval (WBCHIR).
Similarity between the images is ascertained by means of a distance function. The experimental
result shows that the proposed method outperforms the other retrieval methods in terms of
Average Precision. Moreover, the computational steps are effectively reduced with the use of
Wavelet transformation. As a result, there is a substational increase in the retrieval speed. The
whole indexing time for the 1000 image database takes 5-6 minutes.
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
ACKNOWLEDGEMENTS
One of us (*) is grateful to the Prof. Tapodhir Bhattacharjee (VC), Assam University, Silchar for
the award of UGC Fellowship.
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Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
Authors
Ms. Manimala Singha received her B.Sc. and M.Sc. degrees in Computer Science
from Assam University, Silchar in 2005 and 2007 respectively. Presently she is
working, for her Ph.D., as a Research Scholar and her area of interest includes image
segmentation, feature extraction, and image searching in large databases
Prof. K. Hemachandran is associated with the Dept. of Computer Science, Assam
University, Silchar, since 1998. He obtained his M.Sc. Degree from Sri Venkateswara
University, Tirupati and M.Tech. and Ph.D. Degrees from Indian School of Mines,
Dhanbad. His areas of research interest are Image Processing, Software Engineering
and Distributed Computing.
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