Docstoc

Sectorization of Haar and Kekre’s Wavelet for Feature Extraction of color images in Image Retrieval

Document Sample
Sectorization of Haar and Kekre’s Wavelet for Feature Extraction of color images in Image Retrieval Powered By Docstoc
					                                                         (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                      Vol. 09, No.02, 2011



          Sectorization of Haar and Kekre’s Wavelet for
          Feature Extraction of color images in Image
                           Retrieval
                        H.B.Kekre                                                             Dhirendra Mishra
                   Sr. Professor                                                Associate Professor & PhD Research Scholar
 MPSTME, SVKM’s NMIMS (Deemed-to be-University)                             MPSTME, SVKM’s NMIMS (Deemed-to be-University)
       Vile Parle West, Mumbai -56,INDIA                                           Vile Parle West, Mumbai -56,INDIA
               hbkekre@yahoo.com                                                       dhirendra.mishra@gmail.com



Abstract- This paper presents the Innovative idea of                     useful for real-world applications if aggressive attempts are
Sectorization of Haar Wavelet transformed images and                     made. For example, many commercial organizations are
Kekre’s Wavelet Transformed images to extract features                   working on image retrieval despite the fact that robust text
for image retrieval. Transformed images have been                        understanding is still an open problem. Of late, there is
sectored into 4,8,12 and 16 sectors. Each sector produces                renewed interest in the media about potential real-world
the feature vector component in particular sector size.                  applications of CBIR and image analysis technologies,
Thus the feature vector size increases with the increase                 There are various approaches which have been
in the sector size. The experiment of augmenting the                     experimented to generate the efficient algorithm for CBIR
feature vectors with extra components performed .The                     like FFT, DCT, DST, WALSH sectors [8-14][21][22],
performance of proposed method of sectorization                          Transforms [16][17], Vector quantization[17], bit truncation
checked with respect to increase in sector sizes, effect of              coding [18][19].
augmentation of extra components in both Haar and
Kekre’s Wavelet sectorization .The retrieval rate                        The problem of CBIR still needs lots of research to achieve
checked with crossover of average precision and recall.                  the better retrieval performance. It needs extensive
LIRS and LSRR are calculated for average of randomly                     experiments on all of its parameters i.e. Feature extraction,
selected 5 images of all 12 classes and compared with the                similarity measures, retrieval performance measuring
overall average of LIRS/LSRR. The work experimented                      parameters.
over the image database of 1055 images and the
performance of image retrieval with respect to two                        In this paper we have introduced a novel concept of
similarity measures namely Euclidian distance (ED) and                   Sectorization of Haar Wavelet and Kekre’s Wavelet in both
sum of absolute difference (AD) are measured.                            column wise and row wise transformed color images for
Keywords- CBIR, Haar Wavelet, Kekre’s Wavelet                            feature extraction (FE).Two different similarity measures
Euclidian Distance, Sum of Absolute Difference, LIRS,                    namely sum of absolute difference and Euclidean distance
LSRR, Precision and Recall.                                              are considered. Average precision, Recall, LIRS and LSRR
                                                                         are used for performances study of these approaches.
                  I.     INTRODUCTION
Digital world of the current era needs storage and                                        II. HAAR WAVELET [5]
management of bulky digital images. It is the need of the
century to have better mechanism to store, manage and                    The Haar transform is derived from the Haar matrix. The
retrieve whenever needed digital images from the large                   Haar transform is separable and can be expressed in matrix
database. Content-based image retrieval (CBIR), [1-4] is any             form
technology that in principle helps to achieve this motive by                                [F] = [H] [f] [H]T
their visual content. By this definition, anything ranging               Where f is an NxN image, H is an NxN Haar transform
from an image similarity function to a robust image                      matrix and F is the resulting NxN transformed image. The
annotation engine falls under CBIR. This characterization of             transformation H contains the Haar basis function hk(t)
CBIR as a field of study places it at a unique juncture within           which are defined over the continuous closed interval
the scientific community. It is believed that the current state-         t Є [0,1].
of-the-art in CBIR holds enough promise and maturity to be               The Haar basis functions are




                                                                   180                              http://sites.google.com/site/ijcsis/
                                                                                                    ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                 Vol. 09, No.02, 2011

         When k=0, the Haar function is defined as a
         constant
                  hk(t) = 1/√N
         When k>0, the Haar Function is defined as




                                                                               Figure 1: Computation of 4 Sectors

                                                                        B. 8 Sectors Formation

               III. KEKRE’S WAVELET [5]                                 The transformed image sectored in 4 sectors is taken
                                                                        into consideration for dividing it into 8 sectors. Each
Kekre’s Wavelet transform is derived from Kekre’s                       sector is of angle 45o. Coefficients of the transformed
transform. From NxN Kekre’s transform matrix, we can                    image lying in the particular sector checked for the
generate Kekre’s Wavelet transform matrices of size                     sectorization conditions as shown in the Figur2.
(2N)x(2N), (3N)x(3N),……, (N2)x(N2). For example, from
5x5.Kekre’s transform matrix, we can generate Kekre’s
Wavelet transform matrices of size 10x10, 15x15, 20x20
and 25x25. In general MxM Kekre’s Wavelet transform
matrix can be generated from NxN Kekre’s transform
matrix, such that M = N * P where P is any integer between
2 and N that is, 2 ≤ P ≤ N. Kekre’s Wavelet Transform
matrix satisfies [K][K]T = [D] Where D is the diagonal
matrix this property and hence it is orthogonal. The diagonal
matrix value of Kekre’s transform matrix of size NxN can be
computed as                                                                     Figure 2:Computation of 8 Sectors

                                                                        C. 12 Sector Formation.
                                                                         Division each sector of 4 sectors into angle of 30 o
                                                                        forms 12 sectors of the transformed image.
                                                          (2)           Coefficients of the transformed image are divided into
                                                                        various sectors based on the inequalities shown in the
                                                                        Figure 3.



       IV. SECTORIZATION OF TRANSFORMED
                   IMAGES [8-14]


     A. 4 Sector Formation
     Even and odd rows/columns of the transformed images
     are checked for sign changes and the based on which
     four sectors are formed as shown in the Figure 1
     below:
                                                                               Figure 3:Computation of 12 Sectors




                                                                181                            http://sites.google.com/site/ijcsis/
                                                                                               ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                    Vol. 09, No.02, 2011

     D. 16 Sector Formation:

     Similarly we have done the calculation of inequalities
     to form the 16 sectors of the transformed image. The
     even/odd rows/ columns are assigned to particular
     sectors for feature vector generation


              V. EXPERIMENTAL RESULTS

We have used the augmented Wang image database [2] The
Image database consists of 1055 images of 12 different
classes such as Flower, Sunset, Barbie, Tribal, Cartoon,
Elephant, Dinosaur, Bus, Scenery, Monuments, Horses,
Beach. Class wise distribution of all images in the database
has been shown in the Figure 4.

    Class

     No. of
    Images
                45       59       51        100      100                  Figure 6: First 20 Retrieved Images of Row wise Haar
                                                                                            wavelet (16 Sectors)
    Class


     No. of
    Images
               100      100       100       100      100

    Class


     No. of
    Images
               100      100

  Figure 4: Class wise distribution of images in the Image
                          database




Figure5. Query Image

The query image of the class Horse has been shown in
Figure 5. For this query image the result of retrieval of both
Column wise and Row wise Haar and Kekre’s wevlet
transformed images for all sectors are checked. The Figure 6            Figure 7: First 20 Retrieved Images of Row wise Kekre’s
shows the first 20 retrieval for the query image with respect                      Wavelet Sectorization (16 Sectors).
to of Row wise Haar Wavelet Sectorization for its 16
Sectors with sum of absolute difference as similarity                  Once the feature vector is generated for all images in the
measure. It can be observed that the retrieval of first 20             database a feature database is created. 5 randomly chosen
images are of relevant class i.e. Horse; there are no                  query images of each class is produced to search the
irrelevant images till first 45 retrievals in both cases. The          database. The image with exact match gives minimum
result of row wise Kekre’s Wavelet shown in Figure 7; the              absolute difference and Euclidian distance. To check the
retrieval of first 20 images is same as Kekre’s Wavelet                effectiveness of the work and its performance with respect to
except the order of retrieval of images changes.                       retrieval of the images we have calculated the overall




                                                                 182                              http://sites.google.com/site/ijcsis/
                                                                                                  ISSN 1947-5500
                                                        (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                   Vol. 09, No.02, 2011

average precision and recall as given in Equations (3) and
(4) below. Two new parameters i.e. LIRS and LSRR are
introduced as shown in Equations (5) and (6).




                                                                        Figure 8: Overall Average Precision and Recall performance
                                                                           of Sectorization of Row wise Haar Wavelet. Absolute
All these parameters lie between 0-1 hence they can be                   Difference(AD) and Euclidian Distance (ED) as similarity
expressed in terms of percentages. The newly introduced                                          measures.
parameters give the better performance for higher value of
LIRS and Lower value of LSRR [8-13].The class wise
performance of the proposed algorithm with respect to
average precision and recall cross over points in all sectors
for both Haar Wavelet (Row wise and column wise) and
Kekre’s Wavelet (Row wise and column wise) with the
consideration of two similarity measures namely Euclidean
distance(ED) and sum of absolute difference (AD) has been
shown in Figure 8- Figure 11.The average value of each
method has been plotted as horizontal lines to compare the
individual class performances .It is seen that sectorization of
column wise performs better than row wise transformed
images in both HAAR and Kekre’s wavelet. The use sum of
absolute difference gives better retrieval for in all sectors
except 16 sectors compared to Euclidian distance for all
                                                                        Figure 9: Overall Average Precision and Recall performance
classes of images. The retrieval performance for each
                                                                          of Sectorization of Column wise Haar Wavelet. Absolute
classes vary as it is observed that Diana sour, flowers, sunset
                                                                         Difference (AD) and Euclidian Distance (ED) as similarity
and horses have maximum of retrieval i.e. 80%, 70%,50%
                                                                                                  measures
and 50% respectively.
The Figure 12 depicts the overall average performances of
Haar and Kekre’s wavelet. It shows that for sector sizes
4,8,12 Haar wavelet has retrieval performance than Kekre’s
wavelet. The sectorization of column wise transformed
images is far better i.e. on average 45% than row wise i.e. on
average 30%.The performance of the proposed algorithm is
checked with respect to two new parameters i.e. LIRS and
LSRR .The class wise performance of LIRS and LSRR
shown in Figure 13-Figure 20.The class having maximum
value of average precision and recall cross over point must
have maximum LIRS and Minimum LSRR. Taking the
example of Diana sour class which has cross over points as:
80% (Row wise and column wise Haar and Kekre’s
Wavelet), has maximum LIRS (see Figures 13,14,17 and
Figure 18) and Minimum LSRR (see Figures 15,16,19 and
20).Similarly these parameters can be easily checked for                     Figure 10: Overall Average Precision and Recall
other classes as well. Thus these parameters are very useful             performance of Row wise Kekre’s Wavelet Sectorization
to check the performances of the retrieval in CBIR.



                                                                  183                            http://sites.google.com/site/ijcsis/
                                                                                                 ISSN 1947-5500
                                                    (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                              Vol. 09, No.02, 2011

with Absolute Difference (AD) and Euclidian Distance (ED)
                  as similarity measures




                                                                   Figure 13: The LIRS Plot of Row wise Haar transformed
                                                                   images . Overall Average LIRS performances (Shown with
                                                                   Horizontal lines :0.068 (4 Sectors ED), 0.086 (4 Sectors
                                                                   AD), 0.036(8 Sectors ED), 0.038(8 Sectors AD), 0.040(12
                                                                   Sectors ED), 0.066(12 Sectors AD), 0.068(16 Sectors ED),
      Figure 11 Overall Average Precision and Recall
                                                                   0.088(16 Sectors AD) ).
performance of Column wise Kekre’s Wavelet Sectorization
with Absolute Difference (AD) and Euclidian Distance (ED)
                  as similarity measures




                                                                   Figure 14: The LIRS Plot of Column wise Haar transformed
                                                                   images . Overal Average LIRS performances (Shown with
                                                                   Horizontal lines :0.060 (4 Sectors ED), 0.074 (4 Sectors
                                                                   AD), 0.063(8 Sectors ED), 0.089(8 Sectors AD), 0.061(12
   Figure 12: Comparison of Overall Precision and Recall           Sectors ED), 0.078(12 Sectors AD), 0.029(16 Sectors ED),
cross over points of Kekre’s Wavelet and Haar Wavelet with         0.030(16 Sectors AD) ).
 Absolute Difference (AD) and Euclidean Distance (ED) as
                     similarity measure.




                                                             184                            http://sites.google.com/site/ijcsis/
                                                                                            ISSN 1947-5500
                                                     (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                               Vol. 09, No.02, 2011




                                                                    Figure 17: The LIRS Plot of Row wise KWT transformed
Figure 15: The LSRR Plot of Row wise Haar transformed               images . Overall Average LIRS performances (Shown with
images . Overall Average LSRR performances (Shown with              Horizontal lines :0.048 (4 Sectors ED), 0.059 (4 Sectors
Horizontal lines :0.83 (4 Sectors ED), 0.84 (4 Sectors AD),         AD), 0.044(8 Sectors ED), 0.053(8 Sectors AD), 0.067(12
0.87(8 Sectors ED), 0.86(8 Sectors AD), 0.88(12 Sectors             Sectors ED), 0.076(12 Sectors AD), 0.070(16 Sectors ED),
ED), 0.86(12 Sectors AD), 0.64(16 Sectors ED), 0.67(16              0.10(16 Sectors AD) ).
Sectors AD) ).




                                                                    Figure 18: The LIRS Plot of Column wise KWT
Figure 16: The LSRR Plot of Column wise Haar                        transformed images . Overall Average LIRS performances
transformed images . Overall Average LSRR performances              (Shown with Horizontal lines :0.061 (4 Sectors ED), 0.078
(Shown with Horizontal lines :0.63(4 Sectors ED), 0.65 (4           (4 Sectors AD), 0.064(8 Sectors ED), 0.091(8 Sectors AD),
Sectors AD), 0.639(8 Sectors ED), 0.638(8 Sectors AD),              0.066(12 Sectors ED), 0.090(12 Sectors AD), 0.030(16
0.639(12 Sectors ED), 0.633(12 Sectors AD), 0.94(16                 Sectors ED), 0.048(16 Sectors AD) ).
Sectors ED), 0.84(16 Sectors AD) ).




                                                              185                            http://sites.google.com/site/ijcsis/
                                                                                             ISSN 1947-5500
                                                      (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                   Vol. 09, No.02, 2011

                                                                     gives better performance as far as overall average precision
                                                                     and recall cross over point s concerned as compared to
                                                                     Kekre’s Wavelet transform as shown in the Figure 12. The
                                                                     newly introduced parameter LIRS and LSRR gives good
                                                                     platform for performance evaluation to judge how early all
                                                                     relevant images is being retrieved (LSRR) and it also
                                                                     provides judgement of how many relevant images are being
                                                                     retrieved as part of first set of relevant retrieval (LIRS).The
                                                                     sum of absolute difference as similarity measure is
                                                                     recommended due to its lesser complexity and better
                                                                     retrieval rate performance compared to Euclidian distance.

                                                                                         VII. REFERENCES

Figure 19: The LSRR Plot of Row wise KWT transformed                 [1]    Kato, T., “Database architecture for content based
images . Overall Average LSRR performances (Shown with                      image retrieval in Image Storage and Retrieval
Horizontal lines :0.813 (4 Sectors ED), 0.807 (4 Sectors                    Systems” (Jambardino A and Niblack W eds),Proc
AD), 0.085(8 Sectors ED), 0.80(8 Sectors AD), 0.85(12                       SPIE 2185, pp 112-123, 1992.
Sectors ED), 0.80(12 Sectors AD), 0.63(16 Sectors ED),               [2]    Ritendra Datta,Dhiraj Joshi,Jia Li and James Z.
0.67(16 Sectors AD) ).                                                      Wang, “ Image retrieval:Idea,influences and trends
                                                                            of the new age”,ACM Computing survey,Vol
                                                                            40,No.2,Article 5,April 2008.
                                                                     [3]    John Berry and David A. Stoney “The history and
                                                                            development of fingerprinting,” in Advances in
                                                                            Fingerprint Technology, Henry C. Lee and R. E.
                                                                            Gaensslen, Eds., pp. 1-40. CRC Press Florida, 2nd
                                                                            edition, 2001.
                                                                     [4]    Emma Newham, “The biometric report,” SJB
                                                                            Services, 1995.
                                                                     [5]    H.B.Kekre, Archana Athawale and Dipali sadavarti,
                                                                            “Algorithm to generate Kekre’s Wavelet transform
                                                                            from Kekre’s Transform”, International Journal of
                                                                            Engineering,Science           and        Technology,
                                                                            Vol.2No.5,2010 pp.756-767.
                                                                     [6]    H. B. Kekre, Dhirendra Mishra, “Digital Image
Figure 20: The LSRR Plot of Column wise KWT                                 Search & Retrieval using FFT Sectors” published in
transformed images . Overall Average LSRR performances                      proceedings of National/Asia pacific conference on
(Shown with Horizontal lines :0.639 (4 Sectors ED), 0.648                   Information            communication            and
(4 Sectors AD), 0.639(8 Sectors ED), 0.637(8 Sectors AD),                   technology(NCICT 10) 5TH & 6TH March
0.639(12 Sectors ED), 0.637(12 Sectors AD), 0.950(16                        2010.SVKM’S NMIMS MUMBAI
Sectors ED), 0.873(16 Sectors AD) ).                                 [7]    H.B.Kekre, Dhirendra Mishra, “Content Based
                                                                            Image       Retrieval using Weighted      Hamming
                    VI. CONCLUSION                                          Distance Image         hash Value” published in the
                                                                            proceedings of        international conference on
The work experimented on 1055 image database of 12                          contours of computing        technology pp. 305-309
different classes discusses the performance of sectorization                (Thinkquest2010) 13th & 14th March 2010.
of Haar wavelet and Kekre’s wavelet transformed color                [8]    H.B.Kekre, Dhirendra Mishra,“Digital Image
images for image retrieval. The work has been performed                     Search & Retrieval using FFT Sectors of Color
with both approaches of column wise and row wise                            Images” published in International Journal of
transformation. The performance of the proposed method is                   Computer Science and Engineering (IJCSE) Vol.
checked with respect to various sector sizes and similarity                 02,No.02,2010,pp.368-372         ISSN    0975-3397
measuring approaches namely Euclidian distance and sum of                   available                   online                at
absolute difference. It has been observed that the                          http://www.enggjournals.com/ijcse/doc/IJCSE10-
combination of column wise Haar wavelet sectorization with                  02-     02-46.pdf
sum of absolute difference and augmented feature vector



                                                               186                               http://sites.google.com/site/ijcsis/
                                                                                                 ISSN 1947-5500
                                                          (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                    Vol. 09, No.02, 2011

[9]      H.B.Kekre, Dhirendra Mishra, “CBIR using upper                 [ 16 ] A. M. Bazen, G. T. B.Verwaaijen, S. H. Gerez, L.
         six FFT Sectors of Color Images for feature vector                    P. J. Veelenturf, and B. J. van der Zwaag, “A
         generation” published in International Journal of                     correlation-based fingerprint verification system,”
         Engineering and Technology(IJET) Vol. 02, No.                         Proceedings of the ProRISC2000 Workshop on
         02, 2010, 49-54 ISSN 0975-4024 available online                       Circuits, Systems and Signal Processing,
         at                                                                    Veldhoven, Netherlands, Nov 2000.
         http://www.enggjournals.com/ijet/doc/IJET10-02-                [ 17 ] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade,
         02-06.pdf                                                             “Image Retrieval using Color-Texture Features
[ 10 ]   H.B.Kekre, Dhirendra Mishra, “Four walsh                              from         DST on VQ Codevectors obtained by
         transform sectors feature vectors for image retrieval                 Kekre’s Fast       Codebook Generation”, ICGST
         from image databases”, published in international                     International Journal      on Graphics, Vision and
         journal of computer science and information                           Image Processing (GVIP),        Available online at
         technologies (IJCSIT) Vol. 1 (2) 2010, 33-37 ISSN                     http://www.icgst.com/gvip
         0975-9646           available        online         at         [ 18 ] H.B.Kekre, Sudeep D. Thepade, “Using YUV
         http://www.ijcsit.com/docs/vol1issue2/ijcsit201001                    Color Space to Hoist the Performance of Block
         0201.pdf                                                              Truncation Coding for Image Retrieval”, IEEE
[ 11 ]   H.B.Kekre, Dhirendra Mishra, “Performance                             International Advanced Computing Conference
         comparison of four, eight and twelve Walsh                            2009 (IACC’09), Thapar University, Patiala,
         transform sectors feature vectors for image retrieval                 INDIA, 6-7 March 2009.
         from image databases”, published in international              [ 19 ] H.B.Kekre, Sudeep D. Thepade, “Image Retrieval
         journal     of     Engineering,       science     and                 using Augmented Block Truncation Coding
         technology(IJEST) Vol.2(5) 2010, 1370-1374                            Techniques”, ACM International Conference on
         ISSN      0975-5462          available    online    at                Advances in Computing, Communication and
         http://www.ijest.info/docs/IJEST10-02-05-62.pdf                       Control (ICAC3-2009), pp.: 384-390, 23-24 Jan
[ 12 ]   H.B.Kekre, Dhirendra Mishra, “ density                                2009, Fr. Conceicao Rodrigous College of Engg.,
         distribution in walsh transfom sectors ass feature                    Mumbai. Available online at ACM portal.
         vectors for image retrieval”, published in                     [ 20 ] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade,
         international journal of compute applications                         “DST Applied to Column mean and Row Mean
         (IJCA) Vol.4(6) 2010, 30-36 ISSN 0975-8887                            Vectors of Image for Fingerprint Identification”,
         available                   online                  at                International Conference on Computer Networks
         http://www.ijcaonline.org/archives/volume4/numbe                      and Security, ICCNS-2008, 27-28 Sept 2008,
         r6/829-1072                                                           Vishwakarma Institute of Technology, Pune.
[ 13 ]   H.B.Kekre, Dhirendra Mishra, “Performance                      [ 21 ] H.B.Kekre, Vinayak Bharadi, “Walsh Coefficients
         comparison of density distribution and sector mean                    of the Horizontal & Vertical Pixel Distribution of
         in Walsh transform sectors as feature vectors for                     Signature Template”, In Proc. of Int. Conference
         image retrieval”, published in international journal                  ICIP-07, Bangalore University, Bangalore. 10-12
         of Image Processing (IJIP) Vol.4(3) 2010, ISSN                        Aug 2007.
         1985-2304               available       online      at
         http://www.cscjournals.org/csc/manuscript/Journals             [ 22 ] J.L.Walsh, “A closed set of orthogonal
         /IJIP/Volume4/Issue3/IJIP-193.pdf                                     functions”American Journal of Mathematics, Vol
                                                                               45,pp.5-24,year 1923.
[ 14 ] H.B.Kekre, Dhirendra Mishra, “Density distribution
       and sector mean with zero-sal and highest-cal                                      AUTHORS PROFILE
       components in Walsh transform sectors as feature
       vectors for image retrieval”, published in
                                                                                          H. B. Kekre has received B.E. (Hons.)
       international journal of Computer scienece and
                                                                                          in Telecomm. Engg. from Jabalpur
       information security (IJCSIS) Vol.8(4) 2010, ISSN
                                                                                          University in 1958, M.Tech (Industrial
       1947-5500                     available         online
                                                                                          Electronics) from IIT Bombay in 1960,
       http://sites.google.com/site/ijcsis/vol-8-no-4-jul-
                                                                                          M.S.Engg. (Electrical Engg.) from
       2010
                                                                                          University of Ottawa in 1965 and
                                                                        Ph.D.(System Identification) from IIT Bombay in 1970. He
[ 15 ] Arun Ross, Anil Jain, James Reisman, “A hybrid
                                                                        has worked Over 35 years as Faculty and H.O.D. Computer
       fingerprint matcher,” Int’l conference on Pattern
                                                                        science and Engg. At IIT Bombay. From last 13 years
       Recognition (ICPR), Aug 2002.



                                                                  187                             http://sites.google.com/site/ijcsis/
                                                                                                  ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                 Vol. 09, No.02, 2011

working as a professor in Dept. of Computer Engg. at
Thadomal Shahani Engg. College, Mumbai. He is currently
senior Professor working with Mukesh Patel School of
Technology Management and Engineering, SVKM’s
NMIMS University vile parle west Mumbai. He has guided
17 PhD.s 150 M.E./M.Tech Projects and several
B.E./B.Tech Projects. His areas of interest are Digital signal
processing, Image Processing and computer networking. He
has more than 350 papers in National/International
Conferences/Journals to his credit. Recently ten students
working under his guidance have received the best paper
awards. Two research scholars working under his guidance
have been awarded Ph. D. degree by NMIMS University.
Currently he is guiding 10 PhD. Students. He is life member
of ISTE and Fellow of IETE.

                   Dhirendra Mishra has received his BE
                  (Computer Engg) degree from University
                  of Mumbai. He completed his M.E.
                  (Computer Engg) from Thadomal
                  shahani Engg. College, Mumbai,
                  University of Mumbai. He is PhD
                  Research Scholar and working as
Associate Professor in Computer Engineering department of
Mukesh Patel School of Technology Management and
Engineering, SVKM’s NMIMS University, Mumbai,
INDIA. He is life member of Indian Society of Technical
education (ISTE), Member of International association of
computer science and information technology (IACSIT),
Singapore, Member of International association of
Engineers (IAENG). His areas of interests are Image
Processing, Operating systems, Information Storage and
Management




                                                                 188                           http://sites.google.com/site/ijcsis/
                                                                                               ISSN 1947-5500

				
DOCUMENT INFO
Description: The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security. . The core vision of IJCSIS is to disseminate new knowledge and technology for the benefit of everyone ranging from the academic and professional research communities to industry practitioners in a range of topics in computer science & engineering in general and information & communication security, mobile & wireless networking, and wireless communication systems. It also provides a venue for high-calibre researchers, PhD students and professionals to submit on-going research and developments in these areas. . IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.