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Image Retrieval using Row Means of Column Transformed Even and Odd Parts of Image with Walsh, Haar and Kekre Transforms

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Image Retrieval using Row Means of Column Transformed Even and Odd Parts of Image with Walsh, Haar and Kekre Transforms Powered By Docstoc
					                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                      Vol. 08, No.05, 2010

        Image Retrieval using Row Means of Column
       Transformed Even and Odd Parts of Image with
             Walsh, Haar and Kekre Transforms
                                     Dr. H.B.Kekre1, Sudeep D. Thepade2, Akshay Maloo3
                     1
                      Senior Professor, 2Ph.D.Research Scholar & Associate Professor, 3B.Tech (CE) Student
                                          Computer Engineering Department, MPSTME,
                                  SVKM‟s NMIMS (Deemed-to-be University), Mumbai, India
                         1
                           hbkekre@yahoo.com, 2sudeepthepade@gmail.com,3akshaymaloo@gmail.com

Abstract— The paper presents an innovative content based image            [5,18], criminal investigations [23], image search on the
retrieval (CBIR) technique based on row mean of column                    Internet [9,19,20].
transformed even and odd part of image. Here the performance
of proposed CBIR technique is tested for different transforms             A. Content Based Image Retrieval
like Walsh transform, Haar transform and Kekre transform.                      In literature the term content based image retrieval (CBIR)
Instead of using all pixels of image as feature vector for image          has been used for the first time by Kato et.al. [4], to describe
retrieval, row mean of transform applied on columns of odd and
                                                                          his experiments into automatic retrieval of images from a
even part of image is used, resulting into better performance with
                                                                          database by colour and shape feature. The typical CBIR system
much lower computations. The proposed CBIR techniques are
tested on generic image database having 1000 images spread                performs two major tasks [16,17]. The first being feature
across 11. For each transform 55 queries (5 per category) were            extraction (FE), where a set of features, called feature vector, is
fired. Then the precision and recall for all queries are computed.        generated to accurately represent the content of each image in
While considering the relevance of result images for calculation          the database. The second task is similarity measurement (SM),
of precision and recall the results of odd image part and even            where distance between the query image and each image in the
image part both are ORED. The average precision and average               database using their feature vectors is used to retrieve the top
recall values of all queries gives the performance comparison of          “closest” images [16,17]. For feature extraction in CBIR there
proposed CBIR methods as compared to considering full image               are mainly two approaches [5] feature extraction in spatial
data as feature vector or considering the simple row mean as              domain and feature extraction in transform domain. The feature
feature vector. The results have shown the performance                    extraction in spatial domain includes CBIR techniques based
improvement (higher precision and recall values) with proposed            on histograms [5], BTC [1,2,16], VQ [21]. The transform
methods compared to all pixel data of image at reduced                    domain methods are widely used in image compression, as they
computations resulting in faster retrieval. The comparison of             give high energy compaction in transformed image [17,24]. So
transforms for CBIR performance gives Walsh transform                     it is obvious to use images in transformed domain for feature
surpassing the other two considered here.                                 extraction in CBIR [23]. But taking transform of image is time
                                                                          consuming, this complexity is reduced to a great extent by the
   Keywords-content based image retrival (CBIR), row mean,
                                                                          proposed technique. Reducing the size of feature vector using
Walsh transform, Haar transform, Kekre transform.
                                                                          pure image pixel data in spatial domain only and till getting the
                         I.   INTRODUCTION                                improvement in performance of image retrieval is the theme of
                                                                          the work presented. Many current CBIR systems use Euclidean
    The large numbers of images are being generated from a                distance [1-3,8-14] on the extracted feature set as a similarity
variety of sources (digital camera, video, scanner, the internet          measure. The Direct Euclidian Distance between image P and
etc.) which have posed technical challenges to computer                   query image Q can be given as equation 1, where Vpi and Vqi
systems to store/transmit and index/manage image data                     are the feature vectors of image P and Query image Q
effectively to make such large collections easily accessible.             respectively with size „n‟.
Image compression deals with the challenge of storage and
transmission, where significant advancements have been made                                            n
[1,4]. The challenge to image indexing is studied in the context                              ED      (Vpi  Vqi )
                                                                                                      i 1
                                                                                                                       2
                                                                                                                                             (1)
of image database [2,6,7,10,11], which has become one of the
most promising and important research area for researchers
from a wide range of disciplines like computer vision, image                                  II.    IMAGE TRANSFORMS
processing and database areas. The thirst of better and faster
image retrieval techniques is increasing day by day. Some of                  The various transforms [5,24] used for proposed CBIR
important applications for CBIR technology could be identified            techniques are discussed below:
as art galleries [12,14], museums, archaeology [3], architecture
design [8,13], geographic information systems [6], trademark
databases [21,22], weather forecast [5,21], medical imaging




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                                                                                                      ISSN 1947-5500
                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                     Vol. 08, No.05, 2010
A. Walsh Transform                                                      diagonal and diagonal values of Kekre‟s transform matrix are
    The Walsh transform matrix [5,18] is defined as a set of N          one, while the lower diagonal part except the values just below
rows, denoted Wj, for j = 0, 1, ... , N - 1, which have the             diagonal is zero.
following properties:                                                      Generalized NxNKekre‟s transform matrix can be given as:
 • Wj takes on the values +1 and -1.
                                                                                            1        1     1   ..        1           1
 • Wj[0] = 1 for all j.                                                                     N  1   1     1   ..        1           1
                                                                                                                                      
 • Wj xWkT=0, for j k and Wj xWkT =N, for j=k.                                              0      N 2   1   ..        1           1
 • Wj has exactly j zero crossings, for j = 0, 1, ...., N-1.                      K NxN                                                   (2)
                                                                                                                                
 • Each row Wj is either even or odd w.r.t. to its midpoint.                                0        0     0   ..        1           1
                                                                                                                                      
                                                                                            0
                                                                                                     0     0   ..  N    ( N  1)   1
                                                                                                                                       
B. Haar Transform                                                           The formula for generating the term Kxy of Kekre‟s
    This sequence was proposed in 1909 by AlfrédHaar. Haar              transform matrix is:
used these functions to give an example of a countable
orthonormal system for the space of square-integrable                                                                                        (3)
functions on the real line [24]. The study of wavelets, and
even the term "wavelet", did not come until much later. The
Haar wavelet is also the simplest possible wavelet. The
technical disadvantage of the Haar wavelet is that it is not
                                                                                            III.   PROPOSED TECHNIQUE
continuous, and therefore not differentiable. This property can,
however, be an advantage for the analysis of signals with                   The CBIR technique given in [24] has been extended here.
sudden transitions, such as monitoring of tool failure in               In [24] the feature vector considered for CBIR is row mean of
machines.                                                               column transformed image. Here first the odd part and even
                                                                        part of image are obtained using it‟s mirror image. Then
                                                                        transform is applied on each column of obtained images to
C. Kekre‟s Transform                                                    calculate feature vector, image retrieval is done using both
   Kekre‟s transform matrix is the generic version of Kekre‟s           feature vectors (F1 & F2). The obtained results are combined
LUV color space matrix [1,18]. Kekre‟s transform matrix can             using OR operator to obtain the final results. Figure 1 shows
be of any size NxN, which need not have to be in powers of 2            the feature extraction in proposed CBIR technique with row
(as is the case with most of other transforms). All upper               mean [25] of transformed image columns.




        Figure 1 Feature Extraction in Proposed CBIR Technique using row means of column transformed even and odd part of image




                                                                   80                                 http://sites.google.com/site/ijcsis/
                                                                                                      ISSN 1947-5500
                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                     Vol. 08, No.05, 2010

                      R     R    N      R     N     R    Sorted Image Index for Even Part (P1)

                      R     R     N     N      R    R    Sorted Image Index for Odd Part (P2)

                      R     R    N      R     R     R   Result Image Index after using OR operator

                                      Figure 2 How OR operator is used in proposed CBIR technique


   The feature extraction steps for proposed image retrieval
method can be given as follows.
    1.   Calculate the mirror image across Y-axis of the query
         image.
    2.   Using the image and its mirror image to calculate the
         Even Image (P1) and Odd image (P2). When you add
         both P1 and P2 you get the original image back.
    3.   Apply transform T on the column of P1 and P2
         images of size NxN (INxN) to get column transformed
         images of the same size (cINxN)
         cINxN (column transformed) = [TNxN] [INxN]           (4)
    4.   Calculate row mean of column transformed image to
         get feature vector of size N (instead of N2). We get
         two feature vectors for Even and Odd part (F1 & F2).
    5.   Then Euclidean Distance is applied and the results are
         sorted in ascending order for both feature vectors. The                    Figure 3 Sample Images from Generic Image Database
         obtained results are combined using OR operator as                     [Image database contains total 1000 images with 11 categories]
         seen in figure 2, to calculate precision and recall for
         the proposed technique.                                         C. Precision/Recall
                                                                             To assess the retrieval effectiveness, we have used the
    The proposed technique is applied for three different
transforms to see where the best performance is obtained. The            precision and recall as statistical comparison parameters [1,2]
results are compared with applying transform on full image and           for the proposed CBIR techniques. The standard definitions
row mean on image (without column transform).                            for these two measures are given by following equations.

   The figure 2 shows how OR operator is used in proposed                                  Number _ of _ relevant _ images _ retrieved
CBIR techniques. Here the „R‟ is relevant image retrieved and             Pr ecision                                                              (4)
                                                                                            Total _ number _ of _ images _ retrieved
„N‟ is non-relevant image retrieved. These relevancies are then
considered to compute precision and recall values for all
queries.                                                                                    Number _ of _ relevant _ images _ retrieved
                                                                          Re call                                                                 (5)
                                                                                      Total _ number _ of _ relevent _ images _ in _ database

                    IV.   IMPLEMENTATION                                                      V.     RESULTS AND DISCUSSIONS
A. The Platform                                                              For testing the performance of each proposed CBIR
   The implementation of the proposed CBIR techniques is                 technique, per technique 55 queries (5 from each category) are
done in MATLAB 7.0 using a computer with Intel Core 2 Duo                fired on the database of 1000 variable size generic images
Processor T8100 (2.1GHz) and 2 GB RAM.                                   spread across 11 categories. The query and database image
                                                                         matching is done using Euclidian distance. The average
B. Database                                                              precision and average recall are computed and are plotted
   The CBIR techniques are tested on the image database [15]             against number of retrieved images. The crossover point of
of 1000 variable size images spread across 11 categories of              precision and recall gives important performance measure for
human being, animals, natural scenery and manmade things.                image retrieval techniques. Higher the crossover point is better
Figure 3 shows sample image of generic database.                         will be the performance of CBIR method.




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                                                                                                            ISSN 1947-5500
                                                                       (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                               Vol. 08, No.05, 2010




 Figure 4 Crossover Point of Precision and Recall v/s Number of Retrieved         Figure 6 Crossover Point of Precision and Recall v/s Number of Retrieved
                     Images using Walsh Transform                                                     Images using Kekre Transform

    In Figure 4 the crossover points of CBIR using Walsh                               To compare the performance of the considered image
transform applied to the full image as feature vector (referred                  transforms with each other for proposed CBIR techniques, the
as „Full‟), simple row mean as feature vector (referred as                       crossover points of proposed CBIR methods with individual
„RM‟) and the proposed CBIR technique with Walsh transform                       transforms are shown together in figure 6. Here the best
(referred as „TRANSFORM-RM‟ here „WALSH-RM‟) are                                 performance (highest precision-recall crossover point value) is
shown. It can be noted from figure that proposed CBIR                            given by Walsh transform. The performance ranking of
technique with Walsh transform gives best performance than                       transforms could be given as Walsh, Haar and then Kekre
the other discussed methods.                                                     transform. From figure 7 also it can noted that proposed CBIR
                                                                                 technique with all transforms is performing better than CBIR
                                                                                 using full transformed image as feature vector or CBIR using
                                                                                 row mean of image as feature vector.




 Figure 5 Crossover Point of Precision and Recall v/s Number of Retrieved
                      Images using Haar Transform

    The comparison of crossover points of CBIR with Haar
transform of full image as feature vector (Full), row mean of
image as feature vector (RM) and the proposed CBIR                                  Figure 7 Crossover Point of average Precision and Recall v/s proposed
technique with Haar transform (HAAR-RM) are compared in                                         technique for all transforms with Full Image
figure 5. Also here the proposed CBIR method with Haar
transform outperforms other image retrieval techniques.
                                                                                                           VI.    CONCLUSION
    Figure 6 gives the crossover points of CBIR using Kekre                          The new CBIR method using row means of column
transform applied to the full image as feature vector (referred                  transformed odd and even parts of the image with OR operator
as „Full‟), simple row mean as feature vector (referred as                       is proposed. The technique is tested using 55 queries fired on
„RM‟) and the proposed CBIR technique with Kekre transform                       generic image database of 1000 images spread across 11
( „KEKRE-RM‟) are shown. It can be noted from figure that                        categories. The average precision and average recall values of
proposed CBIR technique with Kekre transform gives best                          these queries have proved the proposed CBIR technique better
performance than the other discussed methods.                                    than taking complete transformed image as feature vector and
                                                                                 also bettr than simple row mean of image as feature vector. The



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                                                                                                                 ISSN 1947-5500
                                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                     Vol. 08, No.05, 2010
proposed techniques are implemented using three mage                                      [14] H.B.Kekre, Sudeep D. Thepade, “Color Traits Transfer to Grayscale
transforms like Walsh transform, Haar transform and Kekre                                      Images”, In Proc.of IEEE First International Conference on Emerging
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CBIR method can be given as Walsh followed by Haar and                                    [15] http://wang.ist.psu.edu/docs/related/Image.orig (Last referred on 23 Sept
then Kekre.                                                                                    2008)
                                                                                          [16] H.B.Kekre, Sudeep D. Thepade, “Using YUV Color Space to Hoist the
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[9]    Stian Edvardsen, “Classification of Images using color, CBIR Distance                   http://www.ijest.info/docs/IJEST10-02-06-14.pdf.
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       in Informatics, Norwegian university of science and Technology,                         Comparision of Image Retrieval using Row Mean of Transformed
       Department of computer and Information science, June 2006.                              Column Image ”, International Journal on Computer Science and
[10]   H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “DCT Applied to Row                        Engineering (IJCSE), Volume 2, Issue 5,2010
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       of International Conference on Computer Networks and Security                                                 AUTHORS PROFILE
       (ICCNS), 27-28 Sept. 2008, VIT, Pune.
[11]   H.B.Kekre, Sudeep D. Thepade, Akshay Maloo “Performance                                             Dr. H. B. Kekre has received B.E. (Hons.) in Telecomm.
       Comparison for Face Recognition using PCA, DCT &WalshTransform
                                                                                                           Engineering. from Jabalpur University in 1958, M.Tech
       of Row Mean and Column Mean”, Computer Science Journals,
                                                                                                           (Industrial Electronics) from IIT Bombay in 1960,
       International Journal oj Image Processing (IJIP), Volume 4, Issue II,
                                                                                                           M.S.Engg. (Electrical Engg.) from University of Ottawa in
       May.2010, pp.142-155, available online at http://www.cscjournals.org/
                                                                                                           1965 and Ph.D. (System Identification) from IIT Bombay
       csc/manuscript/Journals/IJIP/volume4/Issue2/IJIP-165.pdf..
                                                                                                           in 1970 He has worked as Faculty of Electrical Engg. and
[12]   H.B.kekre, Sudeep D. Thepade, “Improving „Color to Gray and Back‟                                   then HOD Computer Science and Engg. at IIT Bombay. For
       using Kekre‟s LUV Color Space”, IEEE International Advanced                                         13 years he was working as a professor and head in the
       Computing Conference 2009 (IACC‟09), Thapar University, Patiala,                                    Department of Computer Engg. at Thadomal Shahani
       INDIA, 6-7 March 2009. Is uploaded and available online at IEEE                                     Engineering. College, Mumbai. Now he is Senior Professor
       Xplore.                                                                                             at MPSTME, SVKM‟s NMIMS University. He has guided
[13]   H.B.Kekre, Sudeep D. Thepade, “Image Blending in Vista Creation                                     17 Ph.Ds, more than 100 M.E./M.Tech and several
       using Kekre's LUV Color Space”, SPIT-IEEE Colloquium and                                            B.E./B.Tech projects. His areas of interest are Digital Signal
       International Conference, Sardar Patel Institute of Technology, Andheri,                            processing, Image Processing and Computer Networking. He
       Mumbai, 04-05 Feb 2008.                                                                             has more than 320 papers in National / International
                                                                                                           Conferences and Journals to his credit. He was Senior




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                                                                                                                           ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                               Vol. 08, No.05, 2010
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, Mumbai. He has about than 07 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 has been
on International Advisory Board of many International
Conferences. He is Reviewer for many reputed International
Journals. His areas of interest are Image Processing and
Computer Networks. He has about 79 papers in
National/International Conferences/Journals to his credit
with a Best Paper Award at International Conference
SSPCCIN-2008, Second Best Paper Award at ThinkQuest-
2009 National Level paper presentation competition for
faculty and Best Paper Award at Springer International
Conference ICCCT-2010.


Akshay Maloo is currently pursuing B.Tech. (CS) from
MPSTME, NMIMS University, Mumbai. His areas of
interest are Artificial intelligence, Image Processing,
Computer Networks and Security. He has 13 research papers
in International Conferences/Journals to his credit.




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