784 he Internatonal Arab Journal of Informaton echnology, Vol. 13, No. 6A, 2016 Effcent Color and exture Feature Extracton echnque for Content Based Image Retreval System Jayanth Karuppusamy and Karthkeyan Marappan Department of Electroncs and Communcaton Engneerng, amlnadu College of Engneerng, Inda Abstract: he future user needs n the feld of multmeda retreval s the focus of many research and development actvsts. It s emprcally observed that no sngle algorthm s effcent n extractng all dfferent types of mages lke buldng mages, flower mages, car mages and so on. Hence, a thorough analyss of certan color, texture and shape extracton technques are carred out to dentfy an effcent Content Based Image Retreval (CBIR) technque whch suts for a partcular type of mages. he extracton of an mage ncludes feature descrpton, ndex generaton and feature detecton. he low-level feature extracton technques are proposed n ths paper are tested on Corel database, whch contans 1000 mages. he feature vectors of the Query Image (QI) are compared wth feature vectors of the database mages to obtan Matchng Images (MI). hs paper proposes Color and exture Hstogram (FCH), and Color and Edge Drectvty Descrptor (CEDD) technques whch extract the matchng mage based on the smlarty of color and edge of an mage n the database. he Image Retreval Precson value (IRP) of the proposed technques are calculated and compared wth that of the exstng technques. he algorthms used n ths paper are Dscrete Cosne ransform (DC), Dscrete Wavelet ransform (DW) and lnkng algorthm. he proposed technque results n the mprovement of the average precson value. Also FCH and CEDD are effectve and effcent for mage ndexng and mage retreval. Keywords: CBIR, IRP, FCH, CEDD. Receved January 11, 2014; accepted June 18, 2015 1. Introducton Content Based Image Retreval (CBIR) systems are based on color, texture, shape and edge nformaton are avalable n the lterature. he general applcatons of CBIR are consumer dgtal photo album, dgtal museum, Movng Pcture Experts Group (MPEG-7) content descrptor, general mage collecton for lcensng and natural collectons. hs paper descrbes an mage retreval technque based on mult wavelet texture features. exture s an mportant feature of natural mages. Features of an mage should have a strong relatonshp wth semantc meanng of the mage. CBIR system retreves the relevant mages from the mage data base for the gven Query Image (QI), by comparng the feature of the QI. Edges n an mage consttute an mportant feature to represent ther content of the mage. Human eyes are very senstve to edge features for mage percepton. Hstogram s used to represent an mportant edge feature. An edge hstogram n an mage space represents the drectonalty of the brghtness changes and ts frequency. he normatve MPEG-7 edge hstogram s desgned to contan the 80 bns of local edge dstrbuton. hese 80 bn hstograms are the standardzed semantcs for MPEG-7 Edge Hstogram Descrptor (EHD). he local hstogram bns are not suffcent to represent global features of the edge dstrbuton. he global edge dstrbuton s needed to mprove the retreval performance of an mage and mages n the database. Relevant mages are retreved accordng to mnmum dstance or maxmum smlarty measure calculated between features of QI and every mage n an mage database. CBIR systems are based on many features such as texture, color, shape and edge nformaton. exture contans mportant nformaton about the structural arrangement of surfaces and ther relatonshp to the surroundngs. Varetes of technques are developed for texture analyss. Most of the texture features are obtaned from the applcaton of a local operator, statstcal analyss [14] or measurement n transform doman. 2. Prevous Works An mage can be represented as a set of low level vsual features such as color, texture and shape features. hs work extracts the color and texture feature of an mage n a database. Feature selecton algorthm s based on a fuzzy approach and relevance feedback. he dscrete mage transforms are used for mage data compresson and energy compacton. he energy level n the mage depends on level of colors used. wo dscrete mage transforms are used n ths paper namely Dscrete Hadamard ransform (DH) and Dscrete Wavelet ransform (DW) [4]. hese
Effcent Color and exture Feature Extracton echnque for Content Based Image Retreval System 785 two transforms are appled to dfferent color models namely Hue, Saturaton and Value (HSV) and Y s the luma component and Cb and Cr are the blue-dfference and red-dfference chroma components (YCbCr) separately n a gven large standard database wth 1000 mages formed from 10 dfferent classes taken from the Corel collecton. he second-order Local etra Pattern (LrP) that s calculated based on the drecton of pxels usng horzontal and vertcal dervatves. he proposed method s dfferent from the exstng Local Dervatve Pattern (LDP) n a manner that t makes to use of 0 and 90 dervatves of LDPs for further calculatng the drectonalty of each pxel. he performance resultng from the combnaton of the Gabur ransform (G) and the LrP [2] have been also analyzed, because t s reasonable to assume that low-level vsual features are sampled from a low dmensonal manfold and embedded n a hgh dmensonal space. Hence, the Based Dscrmnate Eucldean Embeddng (BDEE) [5] and ts sem-supervsed extenson to map hghdmensonal samples to a low-dmensonal space are proposed. BDEE precsely preserves both the ntraclass geometry and nterclass dscrmnaton. In-plane rotaton and scale nvarant features [6] of mages n the radon transform doman. he ntal dctonares are learned through ntal clusters that are determned usng the hammng dstance between nearest-neghbor sets of each feature par. Prevous work CBIR s utlzed through calculatng some of the prmtve color features. he ndexng of the mage database s performed wth Self-Organzng Map (SOM) whch dentfed the best matchng unts. color hstogram [3] and subtractve fuzzy clusterng algorthms have been utlzed to dentfy the cluster for whch the QI belongng. hs approach only focus the prmtve color features. Clusterng algorthms have been developed prevously whch ncludes fuzzy means. hs class of algorthms s generalzed to nclude the fuzzy covarances [9, 15]. he resultng algorthm closely resembles maxmum lkelhood estmaton of mxture denstes. It s argued that use of fuzzy co-varances s a natural approach to fuzzy clusterng [1]. Expermental results are presented whch ndcate that more accurate clusterng may be obtaned by usng fuzzy co-varances. Co-ordnate logc flters [13], execute co-ordnate logc operatons among the pxels of the mage. hese flters are very effcent n varous 1D, 2D, or hgherdmensonal dgtal sgnal processng applcatons, such as nose removal, magnfcaton, openng, closng, skeletonzaton and codng, as well as n edge detecton, feature extracton, and fractal modelng. Images of any gven concept are regarded as nstances of a stochastc process that characterzes the concept. he expermental mplementaton, focus on a partcular group of stochastc processes that s the wo-dmensonal Mult resoluton Hdden Markov Models (2D MHMMs). Query refnement based on relevance feedback, suffer from slow convergence and do not guarantee to fnd ntended targets [12]. he feature extracton technques n the exstng systems are: Color Layout Descrptor (CLD) extracton Scalable Color Descrptor (SCD) extracton Edge Hstogram Descrptor (EHD) extracton Auto color correlogram extracton. 2.1. CLD Extracton In CLD [14] extracton process, the mage conssts of three color channels lke R, G and B, whch can be transformed nto YCbCr color space. Each color space wll be agan transformed by 8x8 Dscrete Cosne ransform (DC) matrces of coeffcents, to obtan three 8x8 DC matrces [10, 7]. he CLD descrptor was generated by readng n zgzag order, sx coeffcents from the Y DC matrx and three coeffcents from each DC matrx of the two chromnance components. he descrptor s saved as an array of 12 values. 2.2. SCD Extracton he SCD extracts a quantzed HSV color of the hstogram from a gven mage of RGB format. he probablty values of each bn are calculated and ndexed. he resultng hstogram s transformed nto dscrete Haar transformaton whch can be represented n MPEG-7 standard. he non-unform quantzed array of values sorted. 2.3. EHD Extracton In EHD Extracton, the mage space dvded nto a fxed number of mage blocks. In ths method to extract the edge features, the mage block s appled to dgtal flters n the spatal doman. Frst, dvde the mage block nto four sub blocks as assgnng labels from 0 to 3. he four sub blocks represent the average gray levels at (, ) th mage block respectvely. Also, ths method can represent the flter coeffcents for vertcal, horzontal, 45 degree dagonal, 135 degree dagonal and non drectonal edges of an mage as f v (k), f h (k), f d 45 (k), f d 135 (k), and f nd (k), respectvely, where k=0,, 3 represents the locaton of the sub blocks. he respectve edge magntudes are m v (, ), m h (, ), m d 45 (, ), m d 135 (, ), and m nd (, ) for the (, ) th mage block. hs method recommended for the MPEG-7 standard. 2.4. Auto Color Correlogram Extracton he hghlghts of these features are: It ncludes the spatal correlaton of colors, t can be used to descrbe
786 he Internatonal Arab Journal of Informaton echnology, Vol. 13, No. 6A, 2016 the global dstrbuton of local spatal correlaton of colors, t s easy to compute and the sze of the feature s farly small. o compare the feature vectors by usng dfferent dstance measure. he L1 dstance measure s used commonly to compare the component wse dfference between vectors. he dstance measure s used to calculate the relatve dfferences. In most cases t performs better than the absolute measure. A color correlogram expresses how the spatal correlaton of color changes n pars wth dstance. A color hstogram captures only the color dstrbuton n an mage and does not nclude any spatal correlaton nformaton. 2.5. Paper Outlne In ths paper, new approach for texture and shape based mage retreval based on new ndexng structure and feature extracton technques are presented. he rest of the paper s organzed as follows. Secton 3 s on overvew of proposed approach, ndexng and feature extracton. Secton 4 s mplementaton and secton 5 s the smlarty measure performance evaluaton. Secton 6 s expermental results. Fnally, the concluson s drawn n secton 7. 3. Proposed Approach he proposed approaches are explaned as follows. 3.1. Indexng Indexng s done usng an mplementaton of the document bulder nterface. A smple approach s to use the document bulder factory, whch creates document bulder nstances for all avalable features as well as popular combnatons of features (e.g., all MPEG-7 features or all avalable features). A document bulder s bascally a wrapper for mage features creatng a lucene document from a Java buffered mage. he sgnatures or vectors extracted by the feature mplementatons are wrapped n the documents as text. he document output by a document bulder can be added to an ndex. 3.2. Color and exture Hstogram Color and exture Hstogram (FCH) s a new low level descrptor ncludes n one quantzed hstogram color [8] and texture nformaton. hs features result whch forms the combnaton of three fuzzy unts. Intally the mage s segmented n a preset number of blocks. Each block passes through all the fuzzy unts. he frst unt, extract the fuzzy lnkng hstogram by usng a set of fuzzy rules. hs hstogram stems from the HSV color space. In a three nput fuzzy system, twenty rules are appled n order to generate a 10-bn hstogram; each bn corresponds to a preset color. In the second unt, ths paper proposes a two nput fuzzy system, n order to expand the 10-bn hstogram nto 24-bn hstogram. hus, the nformaton related to the hue of each color s presented. In the thrd unt, each mage block s transformed to the Haar wavelet transform and a set of a texture elements are exported. hese elements are gven to an nput of thrd fuzzy system, whch converts 24-bn hstogram nto a 192- bn hstogram, mportng texture nformaton n the proposed feature. In ths unt eght rules are appled n a three nput fuzzy system. By usng the Gustafson kessel fuzzy classfer, 8-regons are shaped whch are used to quantze the values of the 192 FCH factors n the nterval 1to7, lmtng the length of the descrptor n 576 bts per mage. Fgure 1 show that FCH extracton. Image Block Feature Vector Fgure 1. FCH extracton. 3.3. Color and Edge Drectvty Descrptor A new low-level feature that combnes color and texture nformaton n one hstogram and ts length does not exceed 54 bytes. Frst the mage s separated n a preset number of blocks. In order to extract the color nformaton, a set of fuzzy rules are used to undertake the extracton of a fuzzy lnkng hstogram that was proposed. hs hstogram stems from the HSV color space. wenty rules are appled to a three nput fuzzy system that generates a 10-bn quantzed hstogram. Each bn corresponds to a preset color. he number of blocks assgned to each bn s stored n a feature vector. Fgure 2 show that Color and Edge Drectvty Descrptor (CEDD) Extracton. Image Block Feature Vector Wavelet ransform 10 bns Lnkng Flter 10-bns Lnkng Quantzaton Quantzaton exture 24-bns Lnkng exture 24-bns Lnkng Fgure 2. CEDD extracton. FCH CEDD hen the four extra rules are appled to a two nput fuzzy system, n order to change the 10-bns hstogram
Effcent Color and exture Feature Extracton echnque for Content Based Image Retreval System 787 nto 24-bns hstogram, mportng the nformaton related to the hue of each color presented. he fve dgtal flters are proposed n the MPEG-7 EHD s also used for exportng the nformaton related to the texture of the mage. Each mage block s classfed nto one or more of the sx texture regons that has been fxed and shapng the 144-bns hstogram. he Gustafsan Kessel classfer s used to shape the eght regons whch are used to quantze the values of 144 CEDD factors n the nterval 0 to 7, lmtng the length of the descrptor n 432-bts. he CEDD unt assocated wth color and texture unt. he color unt extracts the color nformaton and the texture unt extracts the texture nformaton. he CEDD hstogram s consttuted by sx regons that can be determned by the texture unt. Each regon s consttuted by 24 ndvdual regons, emanatng from the color unt. he fnal hstogram ncludes 6*24=144 regons. In order to shape the hstogram, frst the mage s separated nto 1600 mage blocks. Each mage block feeds successvely all the unts. If the bn s defned that results from the texture unt as N and the bn that results from the color unt as M, then the mage block s placed n the output hstogram poston as N*24+M. In the exture unt, the mage block s separated nto four regons of sub blocks, the value of each sub block s the mean value of the lumnosty of the pxels that partcpate n t. he lumnosty values are derved from the transformaton the Y component represents the luma nformaton, I and Q represent the chromnance nformaton (YIQ) color space. In the color unt, every mage s transported n the HSV color space. he mean values of H, S and V are calculated and they consttute the nputs of the fuzzy system that shapes the fuzzy 10-bns hstogram. Assume that the classfcaton resulted n the fourth bn, whch dctates the color. hen the second fuzzy system n 24-bn fuzzy lnkng, usng the mean values of S and V, calculate the hue of the color and shapes fuzzy 24-bn hstogram. Assume agan that the system classfes ths blocks n the fourth bn whch dctates the color. he combnaton of the three fuzzy systems are fnally classfy the block n 27-bn (1*24+3). he process s repeated for all the blocks of an mage. At the end of the process, the hstogram s normalzed n the nterval 0 to 1; each hstogram value s quantzed n three bts. 4. Implementaton he paper used mage data set from the MPEG-7 Core Experment (CE), whch has 1000 mages n the database. Most of the mages n the database are natural mages. Low level feature extracton technques focus the color, texture and shape of an mage n a database. he followng feature extracton technques are used to extract the low level color feature n the database. CLD extracton technque whch extracts the color and texture value of an mage n a database. SCD extracton technque whch extracts an mage sze n a database. EHD extracton technque whch extracts an mage shape n a database. Auto color correlogram extracton technque whch extracts the spatal color value n a database. FCH extracton technque whch extracts the color and texture of an mage n a database. CEDD extracton technque whch extracts the color and edge of an mage n a database. he followng algorthms are used for the low level color feature extracton technque n a database. DC s sutable algorthm n color layout and edge hstogram technques for the color extracton. DW algorthm s appled for color moment n FCH technque. lnkng algorthm s appled for color expanson n FCH and CEDD technques. 5. Smlarty Measure Smlarty measurement coeffcent s used to measure the color dstance between the mages n FCH and CEDD extracton CEDD technques. t(, ) Where s the transformed value of pont n an mage and. s the co-ordnate value of frst pont of an mage. Y s the co-ordnate value of second pont of an mage. s the transpose vector of. Y s the transpose vector of Y. In the absolute congruence of the vectors, tanmoto coeffcent takes the value 1; the QI s matched wth the database mage. 6. Expermental Results and Dscussons he low level feature extracton technques proposed n ths paper are tested on Corel database. he QI s used n ths analyss belong to the maor categores lke Butterfly, Rose, Buldng, les, Sunset, Horse, Hlls, Flags, rees and Car. he performance of each technque s measured by calculatng ts Average Image Retreval Precson (IRP) and recall value as gven n Equaton 2 and 3 respectvely. Number of relevent mages retreved IRP otal number of mages retreved Recall= Number of relevent mages retreved No. of relevent mages n the database he focus of all the CBIR technques are manly on the low level mage features lke color, texture and shape. Also, t s found that the performance of the CBIR (1) (2) (3)
788 he Internatonal Arab Journal of Informaton echnology, Vol. 13, No. 6A, 2016 technques s not consstently unform for varous categores of mages. he detaled observatons of performance of varous CBIR technques are lsted n able 1. FCH and auto color correlogram are good for texture mages lke butterfly, sunrse and flag mages. CEDD and edge hstogram are good for natural mages lke buldng, car and ree mages. FCH, CEDD and edge hstogram are good for color mages lke rose mage. he present framework to evaluate CBIR based on recall and precson. able 1. Dfferent types of query mage category comparson. Data Set Color Scalable Edge Auto Color Layout Color Hstogram Correlogram CEDD FCH Parameter IRP Recall IRP Recall IRP Recall IRP Recall IRP Recall IRP Recall Butterfly 45 40 45 40 33 30 56 50 45 40 56 50 Sunrse 33 30 45 40 22 20 22 20 67 60 56 50 Rose 33 30 33 30 67 60 33 30 45 40 45 40 Car 56 60 45 40 45 40 33 30 67 60 33 30 Buldng 56 50 33 30 78 70 67 60 67 60 56 50 Flag 67 70 56 50 11 10 67 60 67 60 78 70 ree 56 50 67 60 56 50 56 50 67 60 56 50 Average IRP 49 Value 47 46 41 45 40 48 43 61 54 54 49 able 1 shows that comparson of IRP and Recall value wth dfferent types of QI category as follows: 1. he average IRP value of color layout s 49%. 2. he average IRP value of scalable color s 46%. 3. he average IRP value of edge hstogram s 45%. 4. he average IRP value of auto color correlogram s 48%. 5. he average IRP value of CEDD s 61%. 6. he average IRP value of FCH s 54%. 7. he average recall value of color layout s 47%. 8. he average recall value of scalable color s 41%. 9. he average recall value of edge hstogram s 40%. 10. he average recall value of auto color correlogram s 43%. 11. he average recall value of CEDD s 54%. 12. he average recall value of FCH s 49%. able 2. Comparson of exstng feature extracton technque wth proposed system. Data set Query Image Category % Image Retreval Precson Value for Exstng System Local Neghborng Movement Based Pyramdal Dscrmnat Wavelet ve Eucldean Decompos Embeddng ton Local etra Patterns % Image Retreval Precson Value for Proposed System CEDD FCH Butterfly 40 25 30 20 45 56 Rose 40 30 44 30 45 45 Car 40 30 40 10 67 33 Buldng 30 35 45 20 67 56 ree 40 45 50 10 67 56 Average IRP Value 38 33 42 18 58 49 able 2 shows that comparson of average precson value wth the exstng technques. 1. Average precson value of local neghborng movement technque s 38%. 2. Average precson value of bas dscrmnatve Eucldean embeddng technque s 33%. 3. Average precson value of pyramdal wavelet decomposton technque s 42%. 4. Average precson value of local tetra patterns technque s 18%. 5. Average precson value of CEDD technque s 58%. 6. Average Precson value of FCH echnque s 49%. he Proposed technques are compared wth the exstng echnque, CEDD and FCH Image Retreval Precson value has mproved. Fgure 3 shows that some sample mages from the Database. Fgure 4 shows that the Query Image. Fgure 5 shows that the frst 9 retreved mages from the database. Fgure 6 shows that the % of Precson value for dfferent types of QI Categores. Fgure 7 shows that the comparsons chart for exstng and proposed system. Fgure 3. Some sample mages from the database. Fgure 4. Query mage. Fgure 5. Frst 9 retreved mages from the database. Fgure 6. % of precson value for dfferent types of query mage categores.
Effcent Color and exture Feature Extracton echnque for Content Based Image Retreval System 789 Fgure 7. Comparson chart for exstng and proposed system. 7. Conclusons and Future Works From all the feature extracton technques, t shows that the performance of FCH and CEDD technques are good n mage retreval. he Proposed technque s compared wth the average precson value of the exstng technques, whch gves the average precson value of CEDD technque s 58% and average precson value of FCH technque s 49%. FCH and CEDD are effectve and effcent for mage ndexng and mage retreval. In future work the low level feature extracton technques lke Back Of vsual Words (BOW) and Seeped Up Robust Feature (SURF) wll mprove the retreval effcency. References [1] Alnhoud J., Content- Based Image Retreval System Based on Self Organzng Map, Color Hstogram and Subtractve Clusterng, he Internatonal Arab Journal of Informaton echnology, vol. 9, no. 5, pp. 452-458, 2010. [2] Ban W. and ao D., Based Dscrmnant Eucldean Embeddng for Content Based Image Retreval, IEEE ransactons on Image Processng, vol. 19, no. 2, pp. 545-554, 2010. [3] Chatzchrstofs S. and Boutals Y., A Hybrd Scheme for Fast and Accurate Image Retreval Based on Color Descrptors, n Proceedng of the Eleventh IASED Internatonal Conference on Artfcal Intellgence and Soft Computng, Palma De Mallorca, pp. 280-285, 2007. [4] Chen Y., Sastry C., Patel V., Phllps P., and Chellappa R., In-Plane Rotaton and Scale Invarant Clusterng Usng Dctonares, IEEE ransactons on Image Processng, vol. 22, no. 6, pp. 2166-2180, 2013. [5] Ch Z., Yan H., and Pham., Algorthms: Wth Applcatons to Image Processng and Pattern Recognton, Advance n Systems- Applcatons and heory, World Scentfc, 1996. [6] Gustafson D. and Kessel W., Clusterng wth a Covarance Matrx, n Proceedng of IEEE Conference on Decson and Control ncludng the 17 th Symposum on Adaptve Processes, Calforna, pp. 761-766, 1978. [7] Kekre H. and Mshra D., Performance Comparson of Sectorzaton of DC and DC Wavelet ransformed Images n CBIR, Internatonal Journal of Computer Applcatons, vol. 23, no. 4, pp. 26-34, 2011. [8] Konstantnds K., Gasteratos A., and Andreads I., Image Retreval Based on Color Hstogram Processng, Optcs Communcatons, vol. 248, no. 4-6, pp. 375-386, 2005. [9] L J. and Wang J., Automatc Lngustc Indexng of Pctures by a Statstcal Modelng Approach, IEEE ransactons on Pattern Analyss and Machne Intellgence, vol. 25, no. 9, pp. 1075-1088, 2003. [10] Lu D., Hua K., Vu K., and Yu N., Fast Query Pont Movement echnques for Large CBIR Systems, IEEE ransactons on Knowledge and Data Engneerng, vol. 21, no. 5, pp. 729-743, 2009. [11] Manunath B., Ohm J., Vasudevan V., and Yamada A., Color and exture Descrptors, IEEE ransactons on Crcuts and Systems for Vdeo echnology, vol. 11, no. 6, pp. 703-715, 2001. [12] Mertzos B. and srkolas K., Co-ordnate Logc Flters: heory and Applcatons Nonlnear Image Processng, Academc Press, 2004. [13] Murala S., Maheshwar R., and Balasubramanan R., Local etra Patterns: A New Descrptor for Content Based Image Retreval, IEEE ransactons on Image Processng, vol. 21, no. 5, pp. 2874-2886, 2012. [14] Reddy P. and Prasad K., Multwavelet Based exture Features for Content Based Image Retreval, Internatonal Journal of Computer Scence and echnology, vol. 2, no. 1, pp. 141-145, 2011. [15] Zmmerman H., Sets, Decson Makng and Expert Systems, Boston MA, 1987.
790 he Internatonal Arab Journal of Informaton echnology, Vol. 13, No. 6A, 2016 Jayanth Karuppusamy receved B.E. Degree n Electroncs and Communcaton Engneerng from Government College of echnology, Bharathyar Unversty, amlnadu, Inda n 1999. She receved her M.E. Degree n Computer and Communcaton from amlnadu College of Engneerng, Annaunversty, amlnadu, Inda n 2008. She s havng 15 years of Academc experence. Her Research nterests nclude Datamnng, Content Based Image Retreval, Natural Image Processng, Computer Networks and Wreless echnology. She s currently workng as Assstant Professor, Department of Electroncs and Communcaton Engneerng, amlnadu College of Engneerng, amlnadu, Inda. Karthkeyan Marappan receved B.E degree n Electroncs and Communcaton Engneerng from Bharathar Unversty, Combatore, Inda n 1990, subsequently M. ech., and Ph.D., degrees n Computer and Informaton echnology from Centre for Informaton echnology and Engneerng, Manonmanam Sundaranar Unversty, runelvel, Inda. He s havng 24 years of experence n varous capactes n both ndustres and academa. Hs research nterests nclude Data mnng, Image Analyss and wreless technology. He has publshed many research papers n varous nternatonal ournals and conferences. He s a Senor Member of IEEE, Secretary of IEEE Podhga subsecton of Madras secton and a lfe member of ISE. He s currently workng as Prncpal at amlnadu College of Engneerng, Combatore, Inda.