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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No.3, 2011
Image Retrieval with Image Tile Energy Averaging
using Assorted Color Spaces
Dr. H.B.Kekre1, Sudeep D. Thepade2, Varun Lodha, Pooja Luthra, Ajoy Joseph, Chitrangada Nemani 3
1
Senior Professor, 2Ph.D.Research Scholar & Associate Professor, 3B.Tech (IT) Student
Information Technology Department, MPSTME,
SVKM‟s NMIMS (Deemed-to-be University), Mumbai, India
1
hbkekre@yahoo.com, 2sudeepthepade@gmail.com,3ajoy.jose90@gmail.com,
chitrangada.nmims@gmail.com, varunlodha_4@hotmail.com, poojapoohluthra@gmail.com
Abstract— Here the feature vector for image retrieval is composed
of average energy of each tile of image for diverse number of image A Content Based Image Retrieval (CBIR) is an interface
tiles (like 1, 4, 9, 16, 25, 36 and 49) considered with the help of between a high level system (the human brain) and a low level
various color spaces. The paper presents exhaustive performance system (a computer). The human brain is capable of performing
comparison of 70 variants of proposed image retrieval technique
complex visual perception, but is limited in speed while a
using ten sundry color spaces and seven image tiling methods is
done with the help of generic image database having 1000 images computer is capable of limited visual capabilities at much higher
spread across 11 categories. For each proposed CBIR technique 55 speeds. In a CBIR, features are used to represent the image
queries (randomly selected 5 per category) are fired on the generic content. The features are extracted automatically and there is no
image database. To compare the performance of image retrieval manual intervention, thus eliminating the dependency on humans
techniques, average precision and recall are computed and plotted in the feature extraction stage. These automated approaches to
against number of retrieved images. The results have shown that object recognition are computationally expensive, difficult and
RGB and HSI color spaces give the best performance for average tend to be domain specific. The typical CBIR system performs
energy based image retrieval across all tiles. Also it has been seen in two major tasks [16,17]. The first one is feature extraction (FE),
all luminance-chromaticity based color spaces ( Kekre’s LUV,
where a set of features, called feature vector, is generated to
YCbCr, YUV, YIQ and Kekre’s YCgCb) that as the number tiles
increased the overall performance also increases. accurately represent the content of each image in the database.
The second task is similarity measurement (SM), where a
Keywords: CBIR, Average Energy, Color Spaces, Image Tiling. distance between the query image and each image in the
database using their feature vectors is used to retrieve the top
I. INTRODUCTION “closest” images [16,17,26]. For feature extraction in CBIR there
The large numbers of images which are being generated from a are mainly two approaches [5] feature extraction in spatial
variety of sources (digital camera, digital video, scanner, the domain and feature extraction in transform domain. The feature
internet etc.) have posed technical challenges for computer extraction in spatial domain includes the CBIR techniques based
systems to store/transmit and index/manage image data on histograms [5], BTC [1,2,16], VQ [21,25,26]. The transform
effectively to make such collections easily accessible. Image domain methods are widely used in image compression, as they
compression deals with the challenge of storage and give high energy compaction in transformed image [17,24]. So it
transmission, where significant advancements have been made is obvious to use images in transformed domain for feature
[1,4,5]. The challenge to image indexing is studied in the context extraction in CBIR [23]. But taking transform of image is time
of image database [2,6,7,10,11], which has become one of the consuming and also needs all images of database to be of same
promising and important research area for researchers from a size to get similar feature vectors. This limitation is overcome
wide range of disciplines like computer vision, image processing here in proposed CBIR methods using average energy concept
and database areas. The thirst for better and faster image with help of image tiling.
retrieval techniques is increasing day by day. Problems with
traditional methods of image indexing have led to the rise in
techniques for retrieving images on the basis of automatically II. CONSIDERED COLOR SPACES
derived features such as color, texture and shape- a technology Including RGB color space, in all ten assorted color spaces are
now referred as Content-Based Image Retrieval (CBIR). Some considered here.
of the important applications for CBIR technology could be
identified as art galleries [12,14], museums, archaeology [3], A. Kekre’s LUV Color Space
architecture design [8,13], geographic information systems [5], Kekre‟s LUV color Space is special case of Kekre Transform.
weather forecast [5,22], medical imaging [5,18], trademark Where L gives luminance and U and V gives chromaticity values
databases [21,23], criminal investigations [24,25], image search of color image. Positive value of U indicates prominence of red
on the Internet [9,19,20]. component in color image and negative value of V indicates
prominence of green component. This needs the conversion of
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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No.3, 2011
RGB to LUV components. The RGB to LUV conversion matrix The YUV to RGB conversion matrix given in equation (6) gives
given in equation 1 gives the L, U, V components of color image the R, G, B components of color image for respective Y, U, V
for respective R, G, B components. components.
L 1 1 1 R
R 0.7492 0.50901 1.1398 Y
U 2 1 1. G (1) G 1.0836 0.22472 0.5876 .U (6)
V 0 1 1 B B 0.97086 1.9729 0.000015 V
The LUV to RGB conversion matrix given in equation 2 gives D. YIQ Color Space
the R, G, B components of color image for respective L, U, V The YIQ color space is derived from YUV color space and is
components. optionally used by the NTSC composite color video standard.
The ‟I‟ stands for in phase and „Q‟ for quadrature, which is the
R 1 2 0 L/3 modulation method used to transmit the color information.
G 1 1 1.U / 6 (2) Y 0.299 0.587 0.144 R
B 1 1 1 V /2 I 0.595716 0.274453 0.321263 . G (7)
Q 0.211456 0.522591 0.31135 B
B. YCbCr Color Space
In YCbCr color Space, Y gives luminance and Cb and Cr gives The inter-conversion equations for YIQ to RGB color space are
chromaticity values of color image. To get YCbCr components given as per the equations (7) and (8).
we need the conversion of RGB to YCbCr components. The R 1 0.9563 0.6210 Y
RGB to YCbCr conversion matrix given in equation 3 gives the G 1 0.2721 0.6474 . I (8)
Y, Cb, Cr components of color image for respective R, G, and B B 1 1.107 1.7046 Q
components.
Y 0.2989 0.5866 0.1145 R
E. Kekre’s YCgCb Color Space
Cb 0.1688 0.3312 0.5000 . G (3) Inter-conversion equations for RGB to Kekre‟s YCgCb color
Cr 0.5000 0.4184 0.0816 B space can be given as below in equations 9 and 10.
The YCbCr to RGB conversion matrix given in equation 4 gives Y 1 1 1 R
the R, G, B components of color image for respective Y, Cb, and Cg 1 1 0 .G (9)
Cr components.
R 1 0.0010 1.4020 Y Cb 1 0 1 B
G 1 0.3441 0.7140 . Cb (4) R 1 1 1 Y /3
B 1 1.7718 0.0010 Cr G 1 1 0 . Cg / 2 (10)
B 1 0 1 Cb / 2
C. YUV Color Space
The YUV model defines a color space in terms of one luminance F. XYZ Color Space
(brightness) and two chrominance (color) components. The YUV Conversion equations for RGB to XYZ color space and XYZ to
color model is used in the PAL, NTSC, and SECAM composite RGB can be given as given in equations 11 and 12 below.
color video standards. Previous black-and-white systems used X 0.412453 0.357580 0.180423 R
only luminance (Y) information and color information (U and V) Y 0.212671 0.71160 0.072169 . G
was added so that a black-and-white receiver would still be able (11)
to display a color picture as a normal black and white pictures. Z 0.019334 0.119193 0.950227 B
YUV models human perception of color in a different way than
the standard RGB model used in computer graphics hardware. R 3.240479 1.537150 0.498535 X
The human eye has fairly little color sensitivity: the accuracy of
the brightness information of the luminance channel has far more G 0.969256 1.875992 0.041556 . Y (12)
impact on the image discerned than that of the other two. The B 0.055648 0.204043 1.057311 Z
RGB to YUV conversion matrix given in equation 5 gives the Y,
U, V components of color image for respective R, G, B
components. G. rgb Color Space (Normalized RGB)
Y 0.299 0.587 0.144 R In order to eliminate the influence of illumination intensity, color
information (R, G and B) can be normalized to get rgb color
U 0.14713 0.22472 0.436 . G (5) space where,
V 0.615 0.51498 0.10001 B
(13)
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I. HSI Color Space
H. HSV Color Space To convert RGB to HSI [29,30], first we convert RGB to
The HSV stands for the Hue, Saturation and Value based on the „normalized rgb‟ using equations given in section 2.2.G. Each
artists (Tint, Shade, and Tone). The Value represents intensity normalized H, S and I are then obtained using following
of a color, which is decoupled from the color information in the equations.
represented image. The Hue and Saturation components are
1
r g r b
h cos 1
intimately related to the way human eye perceives color resulting
, h 0, , forb g
2
in image processing algorithms with physiological basis. 1 (20)
2 r g g b 2
Conversion formula from RGB to HSV is as follows. r g
(14)
1
r g r b
h 2 cos 1 2
, h ,2 , for, b g (21)
1
2
r g r g g b
2
(15)
s 1 3. minr, g , b , s 0,1 (22)
(16)
i ( R G B) /(3.255) , i 0,1 (23)
Conversion from HSV space to RGB space is more complex. For convenience h,s and i values are converted in the ranges of
And, given to the nature of the hue information, we will have a [0,360],[0,100] and [0,255] respectively using following
different formula for each sector of the color triangle. equation 24.
H h 180 / , S s 100, I i 255 (24)
Red-Green Sector:
for
(17) III. IMAGE TILING [24]
Tiling of an image is basically dividing an image into different
equal sized, non overlapping quadrants for feature extraction.
Here seven assorted image tiling techniques are applied on
images for feature extraction per colour space in proposed CBIR
methods.
Green-Blue Sector:
for
(18) IV. PROPOSED CBIR TECHNIQUES
Energy averaging could be defined as saverage of squared values
of pixels of the respective image tile. Here average energy
technique is applied along with tiling for feature vector
generation. Feature vector size for respective image tiling
Blue-Red Sector: method is shown in table 1.
for
(19)
1-Tile 4-Tile 9-Tile 16-Tile 25-Tile 36-Tile 49-Tile
Image Tiling Method
(1x1) (2x2) (3x3) (4x4) (5x5) (6x6) (7x7)
Number of Tiles 1 4 9 16 25 36 49
Feature vector Size 3 12 27 48 75 108 147
Table 1 Feature vector size for respective image tiling method considered
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V. IMPLEMENTATION definitions for these two measures are given by following
The implementation of the CBIR techniques is done in equations.
MATLAB 7.0 using a computer with Intel Core 2 Duo Number _ of _ relevant_ images_ retrieved
Processor T8100 (2.1GHz) and 2 GB RAM. The CBIR Pr ecision
Total _ number_ of _ images_ retrieved (25)
techniques are tested on the image database [15] of 1000
variable size images spread across 11 categories of human
being, animals, natural scenery and manmade things. The Number _ of _ relevant_ images_ retrieved
Re call
categories and distribution of the images are Tribes (85), Total _ number_ of _ relevent _ images_ in _ database
(26)
Buses (99), Beaches (99), Dinosaur (99), Elephants (99),
Roses (99), Horses (99), Mountains (61), Airplanes (100),
Monuments (99) and Sunrise (61). Figure 2 gives the
sample images from generic image database. VI. RESULTS AND DISCUSSION
For testing the performance of each proposed CBIR
technique randomly selected five images from each
category are fired on the database as queries. The average
precision and average recall are computed by grouping the
number of retrieved images. Figure 2 gives performance
comparison of assorted color spaces with average energy
technique based on image tiling. It is seen that as the
image tiling increases so does the performance increase.
The overall best performance is given by HSI color space
in tile-9 (3x3). As far as the color spaces go, the best
results for each of the tiling methods is given by RGB and
HIS color spaces. Also it has been noted that as the no of
tiles goes on increasing in all luminance-chromaticity
Figure 1: Sample images of Generic Image Database [Image based color spaces like Kekre‟s LUV, YCbCr, YUV, YIQ
database contains total 1000 images with 11 categories]
and Kekre‟s YCgCb, the overall performance also
To assess the retrieval effectiveness, we have used the increases
precision and recall as statistical comparison parameters
[1,2] for the proposed CBIR techniques. The standard
1X1 2X2 3X3 4X4 5X5 6X6 7X7
0.42
Crossover Point Value of Average Precision
0.41
0.4
0.39
0.38
0.37
0.36
0.35
and Average Recall
0.34
0.33
0.32
0.31
0.3
0.29
0.28
0.27
0.26
0.25
0.24
0.23
RGB HSI XYZ HSV rgb YCgCb LUV YUV YIQ YCbCr
Assorted Color Spaces Considered for Amendment of Energy Average
Figure 2: Comparison of considered image tiling methods in proposed CBIR techniques for individual color space consideration
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RGB HSI XYZ HSV rgb YCgCb LUV YUV YIQ YCbCr
0.42
Crossover Point Value of Average Precision and
0.41
0.4
0.39
0.38
0.37
0.36
0.35
Average Recall
0.34
0.33
0.32
0.31
0.3
0.29
0.28
0.27
0.26
0.25
0.24
0.23
1X1 2X2 3X3 4X4 5X5 6X6 7X7
Assorted Color Spaces Considered for Amendment of Energy Average
Figure 3: Comparison of color spaces considered in proposed CBIR methods for individual tiling consideration
Figure 3 shows the performance comparison of image tiling VIII. REFERENCES
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Vol. 9, No.3, 2011
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Transform over Row and Column Feature Vectors”,
285 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
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[26] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image „Manshodhan 2010‟ and Best Paper Award at Springer Int. Conf.
Retrieval by Kekre’s Transform Applied on Each Row of ICCCT-2010.
Walsh Transformed VQ Codebook”, (Invited), ACM-
Ajoy Joseph, Chitrangada Nemani, Pooja Luthra, Varun Lodha
International Conference and Workshop on Emerging Trends are currently pursuing B.Tech. (IT) from MPSTME, SVKM‟s
in Technology (ICWET 2010),Thakur College of Engg. And NMIMS University, Mumbai. There area of interest is Content Based
Tech., Mumbai, 26-27 Feb 2010, Invited paper at ICWET Image Retrieval in Image Processing
2010 available at online ACM Portal.
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Processing (ICSAP 2010), IACSIT, Bangalore, pp. 53-57, 9-
10 Feb 2010, The paper is uploaded on online IEEE Xplore.
IX. AUTHOR BIOGRAPHIES
Dr. H. B. Kekre has received B.E. (Hons.) in
Telecomm. Engineering. from Jabalpur University in
1958, M.Tech (Industrial Electronics) from IIT
Bombay in 1960, M.S.Engg. (Electrical Engg.) from
University of Ottawa in 1965 and Ph.D. (System
Identification) from IIT Bombay in 1970 He has worked as
Faculty of Electrical Engg. and then HOD Computer Science and
Engg. at IIT Bombay. For 13 years he was working as a professor and
head in the Department of Computer Engg. at Thadomal Shahani
Engineering. College, Mumbai. Now he is Senior Professor at
MPSTME, SVKM‟s NMIMS University. He has guided 17 Ph.Ds,
more than 100 M.E./M.Tech and several B.E./B.Tech projects. His
areas of interest are Digital Signal processing, Image Processing and
Computer Networking. He has more than 320 papers in National /
International Conferences and Journals to his credit. He was Senior
Member of IEEE. Presently He is Fellow of IETE and Life Member
of ISTE Recently ten students working under his guidance have
received best paper awards and two have been conferred Ph.D.
degree of SVKM‟s NMIMS University. Currently 10 research
scholars are pursuing Ph.D. program under his guidance.
Sudeep D. Thepade has Received B.E.(Computer)
degree from North Maharashtra University with
Distinction in 2003. M.E. in Computer Engineering
from University of Mumbai in 2008 with Distinction,
currently pursuing Ph.D. from SVKM‟s NMIMS
University, Mumbai. He has about than 08 years of
experience in teaching and industry. He was Lecturer in Dept. of
Information Technology at Thadomal Shahani Engineering College,
Bandra(w), Mumbai for nearly 04 years. Currently working as
Associate Professor in Computer Engineering at Mukesh Patel
School of Technology Management and Engineering, SVKM‟s
NMIMS University, Vile Parle(w), Mumbai, INDIA. He is member
of International Association of Engineers (IAENG) and International
Association of Computer Science and Information Technology
(IACSIT), Singapore. He is reviewer for many international journals
and in the international advisory panel for many international
conferences. He has worked as member of International Advisory
Committee for many International Conferences. His areas of interest
are Image Processing and Computer Networks. He has about 100
papers in National/International Conferences/Journals to his credit
with a Best Paper Award at Int. Conference SSPCCIN-2008, Second
Best Paper Award at ThinkQuest-2009 National Level faculty paper
presentation competition, second award for research project at
286 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
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