EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVAL

Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.3, June 2013
DOI : 10.5121/sipij.2013.4302 17
EFFICIENT IMAGE RETRIEVAL USING REGION
BASED IMAGE RETRIEVAL
Niket Amoda and Ramesh K Kulkarni
Department of Electronics and Telecommunication Engineering,
Vivekanand Institute of Technology, University of Mumbai
M.G. Road Fort, Mumbai, India
niketamoda@gmail.com
rk1_2002@yahoo.com
ABSTRACT
Early image retrieval techniques were based on textual annotation of images. Manual annotation of images
is a burdensome and expensive work for a huge image database. It is often introspective, context-sensitive
and crude. Content based image retrieval, is implemented using the optical constituents of an image such
as shape, colour, spatial layout, and texture to exhibit and index the image. The Region Based Image
Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a k-means clustering algorithm
to segment an image into regions. Each region of the image is represented by a set of optical
characteristics and the likeness between regions and is measured using a particular metric function on
such characteristics.
KEYWORDS
Content based image retrieval, K-Means Algorithm, Discrete Wavelet Transform, Region Based Image
Retrieval.
1. INTRODUCTION
Early image retrieval techniques were based on textual annotation of images. By using text
descriptions, images can be arranged by topical or syntactic classification to simplify navigation
and browsing on the basis of standard Boolean queries. It was well admitted that a more
accomplished and direct method to exhibit and index optical information would be based upon
the fundamental characteristics of the images themselves.
Content based image retrieval, is implemented using the optical constituents of an image such as
shape, colour, spatial layout, and texture to exhibit and index the image. In ideal content based
image retrieval systems, the optical characteristics of the images in the database are extracted and
illustrated by multi-dimensional feature vectors. The feature vectors of the images present in the
database result in formation of a feature database.

18
For image retrieval, the users feeds example images or sketched figures to the retrieval system.
The system then converts these examples images into its internal representation of feature
vectors. The similarities / distances between the feature vectors of the query example or sketch
and those of the images available in the image database are then computed and retrieval is
performed using an indexing scheme. The indexing scheme gives an efficient approach to
examine the image database.
Present-day retrieval systems have included users' relevance feedback to adjust the retrieval
process in order to create perceptually and semantically more accurate retrieval results.
An optical content descriptor could be either local or global. A global descriptor uses the optical
characteristics of the whole image, whereas a local descriptor uses the optical characteristics of
regions or objects to describe the image content. In order to acquire the local visual descriptors,
an image is often segmented into parts first.
Some of the widely used techniques for extracting color, texture, shape and spatial relationship
features from images are now described briefly.
Instead of exact matching, content based image retrieval systems calculate the visual similarities
between a query image and the images in a database. The result of the retrieval is not just a single
image but, a list of images arranged according to their similarities with the query image. Different
types of similarity/distance measures will influence the performances of an image retrieval
system considerably. Commonly used similarity measures are: Mahalanobis Distance, Euclidean
Distance and Bhattacharyya Distance.
One of the important issues in content-based image retrieval is effective indexing and faster
image retrieval on the basis of optical characteristics. Since the feature vectors of images tend to
have high dimensionality and hence they are not suitable for conventional indexing structures,
dimension reduction is usually done before setting up an efective indexing scheme. Principal
component analysis (PCA) is also one of the methods commonly used for dimension reduction. In
this method we linearly map input data to a coordinate space, so that the axes are aligned to
reflect the maximum variations in the data.
After reduction of dimension, the indexing of multi-dimensional data is done. There are various
methods available for this purpose, such as R-tree (particularly, R*-tree), K-d-B tree, Linear
quad-trees and Grid files etc.
2. WAVELET TRANSFORMS
The signals produced from natural sources such as digital images often have non-stationary
attributes i.e. their content are variable in time or space.
Frequency analysis of stationary signals can be effectively achieved by projecting the signal onto
a set of infinite spatial extent basis functions using the Fourier transform:
ܺሺ݂ሻ = ‫׬‬ ‫ݔ‬ሺ‫ݐ‬ሻ
ା∞
ି∞
݁ିଶ௝గ௙௧
݀‫ݐ‬ (1)

19
where X(f) represents the global frequency of the signal. Similarly, effective frequency analysis
of non-stationary signals can be achieved by projecting the signal onto a set of spatially localized
basis functions using the wavelet transform.
ߖሺa, bሻ = ‫׬‬ xሺtሻ
ା∞
ି∞
ߖ௔௕
∗ ሺtሻdt,
(2)
where a, b є R and ψab(t) is the translated and scaled version of the mother wavelet ψ(t) given by
ߖ௔,௕ሺ‫ݐ‬ሻ =
ଵ
√௔
ߖ ቀ
௧ି௕
௔
ቁ (3)
Different choices of a and b result in the many different possible wavelet bases at different scales
and translations.
A. Discrete Wavelet Transform (DWT)
The continuous convolution for discrete case the in the above equation is replaced by the
following discrete summation:
ܿ௠,௡ = 〈‫,ݔ‬ ߖ௠,௡〉 ≡ ∑ ‫ݔ‬ሺ݈ሻ௟ ߖ௠,௡ሺ݈ሻ (4)
where cm,n are the wavelet coefficients. The implementation of the convolution of the scaling
function with the signal is done at each scale through the iterative filtering of the signal with a
low pass FIR filter hn. At each scale the approximation coefficients am,n can be obtained using the
following recursive relationship:
ܽ௠,௡ = ∑ ℎଶ௡ି௞ܽ௠ିଵ,௞௞ (5)
where a0,n is the sampled signal itself. In addition to this, if we use a related high pass FIR filter gn
the wavelet coefficients is obtained using the further recursive relation:
ܿ௠,௡ = ∑ ݃ଶ௡ି௞ܽ௠ିଵ,௞௞ (6)
In reconstruction of the original signal, the analysis filters can be selected from a biorthogonal set
having a related set of synthesis filters. The synthesis filters g~
and h~
can be used to perfectly
rebuild the signal using the reconstruction formula:
ܽ௠ିଵ,௟ሺ݂ሻ = ∑ ൣℎ෨ଶ௡ି௟ܽ௠,௡ሺ݂ሻ + ݃෤ଶ௡ି௟ܿ௠,௡ሺ݂ሻ൧௡ (7)
B. Extension of DWT to Two Dimensions
To extend the wavelet transform to two dimensions it is just necessary to separately filter and
down sample in the horizontal and vertical directions. This produces four subbands at each scale.
Denoting the horizontal frequency and followed by the vertical frequency, generates high-high
(HH), high-low (HL), low-high (LH) and low-low (LL) image subbands. We can recursively
apply the same scheme to the low-low subband a multiresolution decomposition can be obtained.

20
Figure 1(a) shows the normal layout of such a wavelet decomposition. The subbands are sensitive
to frequencies at that scale and the LH, HL and HH subbands are sensitive to vertical, horizontal
and diagonal frequencies respectively. Figure 1(b) shows a DWT decomposition of a texture
image. This image shows the variation between wavelet subbands highlighting the scale and
orientation selectivity of the transform. Also, Figure 1(d) shows a DWT decomposition of the
Barbara test image (shown in Figure 1(c)). This image also shows the scale and orientation
selectivity of the DWT. High energy subband regions pick out the texture content at different
scales and orientations.
1 (a) Labelled Subbands 1(b) Magnitude of DWT of Texture image
1 (c) Barbara Test Image 1 (d) Magnitude of DWT of Barbara Image
Fig: 1 Two Dimensional Wavelet Transform
C. Extension of DWT to Two Dimensions
Haar function is one of the oldest and simplest example of a mother wavelet function. It is
composed of a pair of rectangular pulses:
ߖሺ‫ݔ‬ሻ = ൞
1 0 ≤ ‫ݔ‬
ଵ
ଶ
−1
ଵ
ଶ
≤ ‫ݔ‬ 1
0 ‫ݐ݋‬ℎ݁‫݁ݏ݅ݓݎ‬
(8)
A standard two-dimensional Haar wavelet decomposition of an image is very simple to code. It
involves a one-dimensional decomposition on each row of the image, followed by a one-
dimensional decomposition on each column of the result.
3. REGION BASED IMAGE RETRIEVAL
The Region Based Image Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT)
and a k-means clustering algorithm to segment an image into regions. Each region is defined by

21
means of a set of characteristics and the alikeness between the regions is measured using a
specific metric function on such characteristics. The implementation of RBIR can be divided into
two parts: Image Pre-processing and Image Retrieval.
A. Image Pre-processing
The detailed algorithm for the pre-processing stage is given below.
Fig: 2 Flow Diagram
The algorithm requires that all the images in the database and the query image be of the same
size. A size of 128*192 was chosen for all of the images. If the image had a different size, it was
first resized to 128*192 and then the pre-processing operations were carried out on the image.
Start
Read Images into
the Database
Resize each Image to size 128*192
Convert the Images from
RGB to HSV Color Space
Perform 3 Level Haar Wavelet Decomposition
of each Color Channel separately
Implement k-means clustering algorithm
on 3-D Wavelet coefficients of
Approximation Subband of last Level
Using the Mask obtained after Clustering, extract
Regions from the 4 Subbands of the last Level
Calculate the Size, Mean and Covariance of
each Region and store in Feature Database
Stop

22
In the RBIR application, each image is divided into the corresponding color channels (i.e. H, S
and V) and the DWT was applied separately to each color channel. The j-th Wavelet coefficient
of subband B (B є {LL, LH, HL, HH}, where L stands for “low” and H for “high”) and DWT
level l is a 3-D vector i.e.
߱௝
௟;஻
= ሺ߱଴௝
௟;஻
, ߱ଵ௝
௟;஻
, ߱ଶ௝
௟;஻
ሻ (9)
Where each component refers to a color channel c (c є {0, 1, 2}). The energy of wj
l;B
on the c and
d channels is then defined as:
݁௖ௗ௝
௟;஻
= ߱ ௖௝
௟;஻
. ߱ௗ௝
௟;஻
(10)
When c=d, eccj
l;B
is called the channel energy of channel c, whereas when c≠d, ecdj
l;B
is termed the
cross-correlation energy between channels c and d. The energy vector
݁௝
௟;஻
= ൫݁଴଴௝
௟;஻
, ݁଴ଵ௝
௟;஻
, ݁଴ଶ௝
௟;஻
, ݁ଵଵ௝
௟;஻
, ݁ଵଶ௝
௟;஻
, ݁ଶଶ௝
௟;஻
൯ (11)
captures both color and texture information through channel and cross-correlation energies,
respectively. This is known to be one of the most robust methods for the representation of texture
features.
K-Means Clustering: The k-means algorithm segments the observations in the given data into k
mutually exclusive clusters, and returns a vector of indices denoting which of the k clusters it has
assigned each observation.
Each cluster is defined on the basis of its member objects and its centroid. The centroid for
individual cluster is the point to which the sum of distances from all objects in that cluster or
partition is minimized.
ඥ ሺ‫݌‬ଵ − ‫ݍ‬ଵሻ‫ݔ‬ଶ + ሺ‫݌‬ଶ − ‫ݍ‬ଶሻ‫ݔ‬ଶ + … + ሺ‫݌‬௡ − ‫ݍ‬௡ሻ‫ݔ‬ଶమ
= ∑ ሺ‫݌‬௜ − ‫ݍ‬௜ሻ௡
௜ୀଵ (12)
While using the Euclidean distance, each centroid is calculated as the mean of the points present
in the cluster. For example, if the data set is 3 D and the cluster has two points: X = (x1, x2, x3)
and Y = (y1, y2, y3), then the centroid Z becomes Z = (z1, z2, z3), where z1 = (x1 + y1)/2 and z2
= (x2 + y2)/2 and z3 = (x3 + y3)/2.
The k-means algorithm is a two-phase iterative algorithm which minimizes the sum of point-to-
centroid distances, summed over all k segments:
1) In the first phase we use what the literature often describes as "batch" updates, where
each iteration contains reassigning points to their closest segment centroid, all at once,
followed by recalculation of segment centroids. This phase supplies a fast but potentially
only approximate solution as a beginning point for the second phase.
2) In the second phase we use what the literature often describes as "on-line" updates, where
points are independently reassigned if doing so will decrease the sum of distances, and
segment centroids are recalculated after each reassignment. Each iteration during the
second phase contains one pass though all the points.

23
B. Image Retrieval
The detailed algorithm for the Image Retrieval phase is given below.
Fig: 3 Flow Diagram
After reading the query image, the same pre-processing operations of image resizing, RGB to
HSV conversion, DWT decomposition, k-means clustering and feature extraction must be
performed on the query image. At the end of the pre-processing operation, the sizes, mean vectors
and covariance matrices of the regions of the query image would be obtained.
Region Similarity : The similarity between two regions Rq,i (represented by the feature vector
[µRq,i, CRq,i3, size(Rq,i)] of a query image Iq and Rs,j (represented by the feature vector [µRs,j,
CRs,j3, size(Rs,j)]) of a database image Is is computed as
Start
Read Query Image
Perform Pre-processing
operations on Query Image
Compute Region Similarity Scores
between each region of Query Image
and all regions of Database Images
Perform Optimal Region Matching
Compute Image Similarity Scores
Sort the Image Similarity Scores
Display 10
best Results
Stop

24
‫ݎ‬௦௜௠൫ܴ௤,௜, ܴ௦,௝൯ = ݀൫ܴ௤,௜, ܴ௦,௝൯ (13)
where d() is a distance function. The distance d(Rq,i, Rs,j) between the regions Rq,i and Rs,j is a
weighted sum, taken over the four frequency subbands, of the distances between color-texture
descriptors, plus an additional term that takes into account the difference between the relative
sizes of the two regions.
In the present work, all the frequency coefficients are equally weighed i.e. γB = 1 for B є {LL,
LH, HL, HH}. The second term takes into account the difference in size between the regions by
multiplying it with a coefficient that favors matches between large regions. The distance dB(Rq,i,
Rs,j) between two regions on the frequency subband B is computed by using the Bhattacharyya
Metric:
݀஻൫ܴ௤,௜, ܴ௦,௝൯
ଶ
=
1
2
݈݊
‫ۉ‬
‫ۈ‬
‫ۇ‬
ቤ
ܿோ௤,௜
ଷ;஻
+ ܿோ௦,௝
ଷ;஻
2
ቤ
ቚܿோ௤,௜
ଷ;஻
ቚ
ଵ
ଶ
∗ ቚܿோ௦,௝
ଷ;஻
ቚ
ଵ
ଶ
‫ی‬
‫ۋ‬
‫ۊ‬
+
1
8
቎൫ߤோ௤௜
஻
− ߤோ௦௝
஻
൯
்
∗ ൭
ܿோ௤,௜
ଷ;஻
+ ܿோ௦,௝
ଷ;஻
2
൱
ିଵ
∗ ൫ߤோ௤௜
஻
− ߤோ௦௝
஻
൯቏
.............................. (14)
4. IMPLEMENTATION OF RBIR
After the optimal region assignment has been performed, the next step is to compute the Image
Similarity Score. This score is obtained by simply adding the region similarity scores of the
matched regions. The final step is to sort the image similarity scores so obtained and then display
the images having the least distance from the query image.
Adding Images to the Database: The steps involved in adding images to the database are:
1) Run the ‘Feature Database Generation Population’ program. A Matlab GUI appears as
shown in Figure 4
2) Select the images from the folder Image_Database and click on ‘Add’ to generate a
feature database.
3) The images selected will appear in the listbox.
4) Click on the ‘Done’ after selecting the images to be added to the database.
5) Feature Database of the image will be generated.

25
Fig: 4 Feature Database population
Image Retrieval: The steps involved in searching for images are:
1) Start the ‘Region Based Image Retrieval’ program. A GUI appears as shown in Figure
5 (a).
2) Select a query image from the folder Image_Query.
3) The query image will be displayed Figure 5 (b) along with the 16 matches of the most
similar images available in the database.
Fig: 5 (a) Fig: 5 (b)
Fig: 5 Region Based Image Retrieval
5. RESULTS
To test the RBIR application, a database consisting of 180 general images, was used. The 180
images could roughly be categorized into 9 groups, each group consisting of 20 similar images. In
addition 9 query images, each query corresponding to one of the groups were taken.

26
Figure 6 shows an example of the results achieved using the RBIR application. From a semantic
point of view, the results obtained are particularly good i.e. all the images in this particular
example are of horses.
Fig: 6 RBIR Results
A. Partial Match Queries
A partial match query is a query that specifies only part of the image. In Figure 7, the query
image is obtained by cropping a database image. As the results show, the RBIR application gave
the complete image as the very first match.
Fig: 7 Partial Match Quering
B. Scanned Queries
During the scanning process of query image, it may suffer artifacts such as poor resolution,
misregistration, color shift and dithering effects.. To consider the effect of scanned images on the
retrieval effectiveness, the query image was first printed and then subsequently scanned. The
scanned image appeared fuzzier, darker and slightly misregistered compared to the original.
Figures 8 (a) and (b) displays the results obtained by the original query image and the scanned
query image respectively. It can be observed that there is a slight degradation in the quality of the
results obtained.

27
Fig: 8 (a) Result with Original Query
Fig: 8 (b) Result with Scanned Query
Fig: 8 Scanned Queries
C. Difficult Queries
The effectiveness of the RBIR application is confirmed when considering “difficult” queries, i.e.
queries having a low number of similar images in the database. Figure 9 shows the results for a
query having only two similar images in the database. The RBIR system is able to retrieves both
of these images.
Fig: 9 Difficult Queries

28
D. Search Time
For an image retrieval application, the time taken for retrieval is an extremely important
parameter. On the other hand, the time taken for pre-processing is not as important since the pre-
processing operations have to be carried out only once.
The first entry in table below shows the average time taken to perform the pre-processing
operations on the database of 180 images of size 128*192. The second entry shows the average
time taken during the image retrieval phase. Here again, the query image was of size 128*192.
The experimental setup consisted of a computer with a 2.4 GHz Intel(R) Core(TM)2 Duo CPU
and 3 GB of DDR2 PC RAM running MATLAB 7 on Windows 7 Eternity.
Pre-processing Stage 25.2 seconds
Image Retrieval Stage 1.7 seconds
6. CONCLUSIONS
Although the HSV color space was found to give better results compared to the RGB color space
in, in our experiments the RGB and HSV color spaces were found to give almost equivalent
results. Eventually, it was decided to use the HSV color space because it gave better results than
the RGB color space in case of “difficult queries”. The figure below shows that when using the
RGB color space, only one of the two matches was retrieved. On the other hand, in the HSV color
space, both the matches were retrieved.
REFERENCES
[1] D.Lowe, “Object recognition from local scale-invariant features,” in ICCV, 1999, pp. 1150–1157.
[2] Y.J.Zhang “A survey on evaluation methods for image segmentation”, Pattern Recognition 29 (8)
(1996) 1335 - 1340
[3] A.Jain, “Data clustering: 50 years beyond k-means,” Pattern Recognition Letters, vol. 31, no. 8, pp.
651 – 666, June 2010.
[4] W.Zhao, H.Ma, Q.He, "Parallel K-Means Clustering Based on MapReduce," in: Cloud Computing,
vol. 5931, pp. 674-679, 2009.
[5] W.D.Arthur, S. Vassilvitskii, “K-means++: the Advantages of careful seeding,” in Proc. 2007
Symposium on Discrete Algorithms, pp.1027-1035.
[6] Rafael C. Gonzalez, Richard E. Woods, " Digital Image Processing" , Second Edition, Prentice Hall
Upper Saddle River, New Jersey 07458, TA1632.G66 2001, 698-740
[7] Fast Multiresolution Image Querying, International Conference on Computer Graphics and
Interactive Techniques, 1995: Charles E.Jacobs, Adam Finkelstein, David H. Salesin

[8] Content-based Image Retrieval, A report to
John Eakins, Margaret Graham
[9] Fundamentals of Content-based Image Retrieval, Multimedia Information Retrieval and Management
- Technological Fundamentals and Applications, Springer, 2002: Dr.Fuhui Long
Zhang, Prof. David Dagan Feng
[10] Image Retrieval – Current techniques, Promising directions and Open issues, Journal of Visual
Communication and Image Representation, 1999: Yong Rui, Thomas S.Huang, Shih
[11] Wavelet Based Texture Analysis and Segmentation for Image Retrieval and Fusion, Thesis,
University of Bristol, 2002: Paul R. Hill
[12] WINDSURF: A Region Based Image Retrieval System, Proceedings of the 10th International
Workshop on Database & Expert Systems Applicati
Patella
[13] P.Felzenszwalb, R.Girshick, D.McAllester, and D.Ramanan, “Object detection with discriminatively
trained part based models,”in IEEE Transactions on Pattern Analysis and Machine Intelligence, v
32, 2010.
Authors
Niket Amoda received his B.E. in Electronics & Communication Engineering from
Rajiv Gandhi Proudyogiki Vishwavidyalaya, B
completed his Diploma in Advance Computing from ACTS, Pune, India in 2008
and is M.E. scholar in Department of Electronics & Telecommunication
Engineering, Vivekanand Education Society's Institute of Technology (VESIT),
University of Mumbai, India. He worked as a Software Development Engineer in
Ford Motor Company, India from 2008 to 2009. He is currently working as a
Assistant Professor in Electronics & Telecommunication Engineering Department
of KC College of Engineering & Manag
publications in international journals and international conferences. His research area is Image processing.
He is also engaged in SAP certification from SAP Germany.
Dr. R. K. Kulkarni completed his PHD from, National Institute of Technology,
Rourkela, Orissa, India. He received his bachelor degree in Electronics &
Communication from Mysore University and master degree in Digital Electronics
form Karnataka University, Karnataka. H
journals and international conferences. His research area is Image processing, Non
– Linear filters, and Digital signal processing.
based Image Retrieval, A report to the JISC Technology Applications Programme, 1999:
John Eakins, Margaret Graham
based Image Retrieval, Multimedia Information Retrieval and Management
Technological Fundamentals and Applications, Springer, 2002: Dr.Fuhui Long, Dr. Hongjiang
Zhang, Prof. David Dagan Feng
Current techniques, Promising directions and Open issues, Journal of Visual
Communication and Image Representation, 1999: Yong Rui, Thomas S.Huang, Shih-Fu Chang
Texture Analysis and Segmentation for Image Retrieval and Fusion, Thesis,
University of Bristol, 2002: Paul R. Hill
WINDSURF: A Region Based Image Retrieval System, Proceedings of the 10th International
Workshop on Database & Expert Systems Applications, 2000: Ilaria Bartolini, Paolo Ciaccia, Marco
P.Felzenszwalb, R.Girshick, D.McAllester, and D.Ramanan, “Object detection with discriminatively
trained part based models,”in IEEE Transactions on Pattern Analysis and Machine Intelligence, v
Niket Amoda received his B.E. in Electronics & Communication Engineering from
Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India, in 2006. He
completed his Diploma in Advance Computing from ACTS, Pune, India in 2008
and is M.E. scholar in Department of Electronics & Telecommunication
Engineering, Vivekanand Education Society's Institute of Technology (VESIT),
y of Mumbai, India. He worked as a Software Development Engineer in
Ford Motor Company, India from 2008 to 2009. He is currently working as a
Assistant Professor in Electronics & Telecommunication Engineering Department
of KC College of Engineering & Management & Research, Thane, India, since 2010 till date. He has many
He is also engaged in SAP certification from SAP Germany.
completed his PHD from, National Institute of Technology,
Rourkela, Orissa, India. He received his bachelor degree in Electronics &
Communication from Mysore University and master degree in Digital Electronics
form Karnataka University, Karnataka. He has many publications in international
journals and international conferences. His research area is Image processing, Non
Linear filters, and Digital signal processing.
29
the JISC Technology Applications Programme, 1999:
based Image Retrieval, Multimedia Information Retrieval and Management
, Dr. Hongjiang
Current techniques, Promising directions and Open issues, Journal of Visual
Fu Chang
Texture Analysis and Segmentation for Image Retrieval and Fusion, Thesis,
WINDSURF: A Region Based Image Retrieval System, Proceedings of the 10th International
ons, 2000: Ilaria Bartolini, Paolo Ciaccia, Marco
P.Felzenszwalb, R.Girshick, D.McAllester, and D.Ramanan, “Object detection with discriminatively
trained part based models,”in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.
since 2010 till date. He has many

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