Available online at ScienceDirect. Procedia Computer Science 46 (2015 )

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1 Avalable onlne at ScenceDrect Proceda Computer Scence 46 (2015 ) Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014) Pattern Extracton n Segmented Satellte Images By Reducng Semantc Gap Usng Relevance Feedback Mechansm Deepka NP a, *, Lekshm Subha MS a, Vj Gopal a a Department of Computer Scence and Engneerng, SCMS School of Engneerng and Technology, Ernakulam, Kerala, , Inda Abstract Image Segmentaton dvdes a regon nto sub regons n such a way that the pxels n a sub regon are smlar. Satellte mage segmentaton, an applcaton n mage segmentaton s to extract the patterns usng mage retreval systems. Image Retreval can be of a wde area n pattern recognton. Content-based can be of best form for retreval of satellte mages. Extracton of hghlevel features from the mages n the form of query or keywords and reduces the semantc gap between the low-level features. To produce better accuracy, Relevance Feed-back based CBIR methods are used for reducng the semantc gap. The overall correlaton between the taken satellte mage and processed mage s dentfed usng Correlaton Rato. Ths paper focuses on the mplementaton of K-Means Clusterng along wth RF mechansm checks the smlarty between orgnal and extracted pattern The The Authors. Authors. Publshed Publshed by by Elsever Elsever B.V. B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014). Technologes (ICICT 2014) Keywords: Image Pre-processng;K-Means Clusterng;Relevance Feedback;Feature Extracton;Pattern Extracton;Correlaton Rato 1. Introducton Image Segmentaton s the process of dvdng a regon nto sub-regons or changng the representaton nto somethng whch s easer to analyze and more understandable. Image segmentaton s nothng but pxel classfcaton. It s one of the major task n pattern recognton and stll a major ssue n mage analyss and mage * Correspondng Author. Tel.: E-mal address:deepka.dsec@gmal.com The Authors. Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014) do: /j.procs

2 1810 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) processng for object trackng, face detecton, mage retreval. Image Segmentaton can be classfed nto two: smlarty and dscontnuty. In smlarty method, mage regon s classfed nto sub regons n such a way that pxels belongng to a sub regon are smlar and dssmlar from rest of sub regons. One of such smlarty method s K-Means Clusterng 3,6 unsupervsed algorthm. Intally uses Text Based Image Retreval Systems. But, t requres human effort. As an mpact of ths Content Based Image Retreval systems came nto beng. In Content-based, mages refers to colour, texture, shape, or any nformaton that can be obtaned from mage tself 1,2 (low-level features). When CBIR came nto beng, the need for a user frendly system became apparent. What the user thnks (human ntellgence) s hgh-level semantcs. Lowlevel features 16 are used to extract mage because, hgh level features are dffcult to extract and moreover they provde addtonal nformaton about objects n an mage. Semantc gap 2,8 s defned as the dfference between lowlevel features 4,8 and hgh-level features. The semantc gap between features totally affects the performance of CBIR systems. Many solutons ams to brdge the semantc gap thus mprovng the performance of overall system. Two solutons to address ths problem are user s Relevance Feed-back mechansm and Feature Selecton 1. Relevance Feed-back 11 mechansm s the process of provdng more and more feed-back usng query, keywords to the system by the user. RF mechansm enables to mprove the nteracton between user and system thus refnng the results at each teraton. Ths can also be applcable n medcal-mages n whch mages havng smlar structure but dffer n the way the dseases develops wth small dfferences often create confusons n experts. Feature Selecton can reduce the gap by provdng the features n the form of a query and removes rrelevant features. By provdng the query for a partcular feature, t searches n the database for that feature n such a way that the retreved mages wll be closer to the gven query. Fnally, the pattern s extracted and a smlarty checkng s done between the orgnal mage and the extracted pattern usng Correlaton Rato. 2. Related Work Ansa Saju 1 proposes relevance feedback mechansm ncorporated n content based retreval manly amed at reducng the semantc gap reducton between features. The effcency of entre system s calculated usng two measures: Precson and Recall. Shv Ram Dubey 3 proposes a novel based defect segmentaton of frut usng K-Means Clusterng s proposed. Ths proposed method s carred out n two stages. Frstly, smlar pxels are grouped nto a cluster. Secondly, the clusters are grouped nto specfc number of regons. The author proposes ths method as the frut defect dentfcaton done by manually requres more tme. Yu sun 9 proposes a relevance feedback along wth onlne feature selecton n content based mage retreval systems for narrowng the semantc gap between those features. In order to gude the onlne feature selecton method, nconsstency measurement s used. Fnal results show wth hgher accuracy rate. Xaoqan Xu 11 proposes a novel-based feedback mechansm for retreval of shapes n spne X-ray mages. Along wth ths, a short-term memory approach s presented for the removal of redundant request of user s feedback. Smlarty of both full and partal shapes of spnes are presented usng a shape smlarty measure. Updaton of weght procedure s done to make the mages sutable for user s to provde feedback.

3 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) Proposed Methodology The proposed methodology nvolves sx sectons: Database Collecton, Image Pre-processng, Segmentaton, Feature Extracton, Relevance Feedback 16, and Smlarty Checkng. Satellte mage database of fve dfferent classes each wth 50 mages were taken nto consderaton. Pre-processng of satellte mages are done through flterng functons. Segmentaton s acheved by K-Means Clusterng. Feature set nvolves Centrod, RGB range and Shape. Relevance Feedback 14,9 can be acheved by user s ntenton to extract a partcular pattern n an mage. Fnally, Correlaton Rato provdes for the Smlarty Checkng. The dagrammatc representaton of proposed method s gven n Fg.1 and each secton n the proposed method s explaned n subsecton 3.1, 3.2, 3.3, 3.4, 3.5. Fg. 1. Dagrammatc representaton of proposed method The steps followed n proposed method are as follows:- Satellte Image s taken from the database

4 1812 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) Do the mage enhancement procedures usng flterng technques Segment mage usng K-Means Clusterng. Here used 3 clusters for processng Extract the low-level features of the clusters usng query. System gves the retreval results based on query The system asks whether user s satsfed wth the results. If yes, go wth shape extracton of patterns Smlarty of the retreved pattern and the orgnal mage s checked usng Correlaton-Rato Else, select the cluster wth the pattern and agan segment the retreved cluster usng K-Means and refne the results usng steps 4,5,6,7 3.1 Image Pre-processng When mages captured by camera or any sensng systems, dstortons can occur due to change n the ntensty levels or due to poor llumnaton or poor contrast level. Image Pre-processng 12 can brng out certan features n an mage. It can hghlght certan characterstcs so that the results more accurate and precse than the orgnal mage for the applcaton. mflter 1 s used for flterng an mage 1. The fspecal command s to create two-dmensonal pre-defned flter. Table.1 gves certan flter 1 functons used to enhance an mage. Table 1. Image Flterng Functons Value Average Dsk Guassan Laplacan Prewt Sobel Unsharp Descrpton Averagng flter Crcular averagng flter Guassan low pass flter Approxmates two dmensonal laplacan operator Prewt horzontal edge-emphaszng flter Sobel horzontal edge-emphaszng flter Unsharp contrast enhancement flter 3.2 K-Means Clusterng K-Means Clusterng 10 s one of the unsupervsed-algorthm. Unsupervsed algorthm means the user can defne the number of clusters for clusterng. It classfes the mage nto n number of clusters as per the user s decson. Also defne K-centrods, each for each cluster. Centrods must be placed as far away from each other. The next step s the calculate the Eucldean dstance 13 between pxels and centrod and assgn each of the pxel locaton to the nearest centrod. When no pont s left, early group age s done. Secondly, calculate the new K-centrod locaton by takng the mean of all the pont n the cluster. Ths process s repeated untl no change n centrod happens. The data n each cluster share smlar propertes 13. The algorthm for the K-Means Clusterng s gven below: Defne number of clusters and K-centrod locaton Calculate the Eucldean dstance between each pxel and cluster centrod and assgn each pxel to the nearest centrod When no more ponts to process, calculate the new centrod n each cluster by takng the mean of each pont Repeat the step 2 and 3 untl no change n centrod locaton. At ths pont clusterng stops and clusters are stable.

5 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) The am of proposed method s to segment the satellte mages usng K-Means Clusterng and L*a*b color space. Here, used three clusters for segmentaton. The steps used to segment the mage nto clusters are gven below: Read the RGB level mage Convert the RGB mage to L*a*b* color space because ths color space ncludes all percevable colors and also enables to quantfy the vsual dfferences between the colors. L* represents the Lumnosty layer and the color nformaton s avalable n a* and b* layers only. Usng K-Means classfy the colors and the dstance between the colors can be computed usng Eucldean dstance. Every pxel n the mage obtaned from step3 can be labeled usng the cluster-ndex. Group the pxels wth the same cluster-ndex by color nto dfferent mages, based on number of clusters. After performng K-Means Clusterng, next step s to mprove the retreval effcency of satellte mages by narrowng the semantc gap between both low-level and hgh-level features. Ths can be done through the stages mentoned below: 3.3 Feature Extracton The features 1,12 used for extractng the patterns n an mage nclude Centrod of cluster, Range of RGB n each cluster, shape of a pattern Centrod Centrod specfes the center of mass of a regon. It s a 1-by Q vector. The frst element s the horzontal component(x-coordnate) and second component s the vertcal component(y-coordnate) of center of mass Range of RGB The RGB 2 value ranges from The red, green, blue n full ntensty makes whte. Accordng to the user s ntenton, select the cluster wth the specfed pattern and calculate RGB value at each pxel locaton from the color hstogram. From the retreved results, fnd the mnmum and maxmum range of RGB Shape Select the cluster wth specfed pattern. Identfy ponts n the cluster where the pattern les by postonng the cursor wth the mouse. Thus, receves unlmted number of ponts, returns the x and y coordnates n vector format. Create a bnary ROI (Regon of Interest) mask wth the returned coordnate postons. Subtract the orgnal mage and the mask to return the specfed pattern from the cluster. 3.4 Relevance Feedback RF 1,8,9 mechansm ncreases the nteracton between the user and the system provdng feedback 11 to the system. User can decde when to stop the feedback 15,17. It nvolves the followng steps 15. Provde feedback 5,7,17 to the system n the form of keywords, query, and example The user decdes whether the dsplayed mages are relevant or rrelevant. If they are rrelevant, system learns and tres agan. Move to step 2. Stop executon, untl retreved mages becomes relevant and extract the shape for the pattern n the cluster.

6 1814 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) Smlarty Checkng Smlarty of extracted pattern and orgnal mage s calculated usng correlaton-rato. Value les between 0 and Value nearer to 1 shows hgh correlaton (smlarty). Correlaton-Rato shown n equaton(3) s calculated from equaton(1) and equaton(2). Standard Devaton can be fnd out usng 1 n x Y x m (1) Varance of s gven by, D n n 0 2 Where, n 255 n 0 Correlaton rato(cr) can be fnd out usng, (2) CR 2 Squareroot 1 D (3) 4. Experments and Results a b c d e Fg. 2. (a) Orgnal mage; (b) Cluster1formed wth feature vector cluster centrod co-ordnate (Centrod : ) n frst teraton; (c) Cluster2 formed wth feature vector cluster centrod co-ordnate (Centrod : ) n frst teraton ; (d) Cluster3 formed wth feature vector cluster centrod co-ordnate (Centrod : ) n frst teraton ; (e) Extracted Pattern.

7 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) Experment s done usng satellte mage database of 5 dfferent classes each wth 50 mages. These classes nclude buldng(50),lakes(50),slands(50),cyclones(50),glacer(50). Fgure 2 shows the result for buldng dataset. From fgure.2. (a) s the orgnal mage. Here, we tres to retreve blue pattern from Fg.2. (a) whch s n the form of a brd by gvng dfferent feedback n the form of a query. For ths, frst perform K-Means Algorthm and extracted frst feature e, cluster coordnate from the formed clusters and the output s shown n Fg.2. (b), Fg.2. (c), Fg.2. (d). After performng the frst teraton, the searched pattern s found n cluster1. So agan, segment the cluster1 nto three clusters usng K-Means clusterng to refne the results by performng relevance feedback wth RGB as the next feature n the formed clusters n the second feedback. After performng the second feedback, dentfy the cluster wth the needed pattern and extract the pattern usng shape parameter shown n Fg.2. (e). It s the user who decdes when to stop the teraton. Ths can be performed n all knds of datasets n search for a pattern. Fnally, a smlarty value of s obtaned from correlaton-rato showng hgh smlarty between orgnal mage and extracted pattern. 5. Concluson The experment s conducted on dfferent mage databases n search for a partcular pattern. Here shown only for a sngle mage database e,buldng The expermental results shows that the patterns can be well-obtaned by reducng (narrowng) the semantc gap between the low-level features and hgh-level features usng the proposed Relevance Feedback mechansm wth K-Means Clusterng. Eventhough on each teraton features are extracted, fnally a hgh smlarty s obtaned between orgnal mage and extracted pattern. Future work ncludes creaton of a dataset wth smlar mages. References 1. Ansa Saju, Thusnavs Bella Mary.I., A.Vasuk, Reducton of Semantc Gap Usng Relevance Feedback Technque n Image Retreval System, Grshma Y. Bobhate, Usha A. Jogalekar, Reducton Of The Semantc Gap Usng Pseudo Relevance Feedback Algorthm, Internatonal Journal of Advanced Computatonal Engneerng and Networkng, Vol 72-No11, Aprl Shv Ram Dubey, Pushkar Dxt, Nshant Sngh, Jay Prakash Gupta, Infected Frut Part Detecton usng K-Means Clusterng Segmentaton Technque, Internatonal Journal of Artfcal Intellgence and Interactve Multmeda, S.Vashnav, Dr.T.T. Mrnalnee and Tna Esther Trueman, CBIR usng Relevance Feedback Retreval System, Internatonal Conference on Computng and Control Engneerng (ICCCE), Aprl, G. Svakamasundar, V. Seenvasagam, Dfferent relevance feedback technques n CBIR: A survey and comparatve study, 2012 Internatonal Conference on Computng, Electroncs and Electrcal Technologes (ICCEET) 6. J.SelvaKumar, A.Lakshm, T.Arvol, Bran Tumor Segmentaton and Its Area Calculaton n Bran MR Images usng K-Mean Clusterng and Fuzzy C-Mean Algorthm, IEEE-Internatonal Conference On Advances In Engneerng, Scence And Management (ICAESM), 2012 March, Ja-Hwung Su, We-Jyun Huang, Phlp s. Yu, and Vncent S. Tseng, Effcent Relevance Feedback for Content Based Image Retreval by Mnng User Navgaton Patterns, IEEE Trans. On Knowledge and Data Engg., March Pushpa B. Patl, Manesh B. Kokare, Relevance Feedback n Content Based Image Retreval: A Revew, Journal of Appled Computer Scence & Mathematcs, Yu sun, Br Bhanu, Image retreval wth feature selecton and relevance feedback, Proceedngs of 2010 IEEE 17th nternatonal conference on mage processng September p , 2010, Hong Kong 10. Anl Z.Chtade, DR. S.K. Katyar, Color Based Image Segmentaton Usng K-Means Clusterng, Internatonal Journal of Engneerng Scence and Technology, 2010

8 1816 N.P. Deepka et al. / Proceda Computer Scence 46 ( 2015 ) Xaoqan Xu, Dah-JyeLee, SameerK.Antan, L.RodneyLong, JamesK.Archbald, Usng relevance feedback wth short-term memory for content-based spne X-ray mage retreval, Neurocomputng 72 (2009) p Hayng Guan, Sameer Antan, L. Rodney Long, and George R. Thoma, Brdgng the Semantc Gap Usng Rankng SVM for Rankng SVM for mage retreval, IEEE, Suman Tatraju, Av Mehta, Image Segmentaton usng k-means clusterng, EM and Normalzed Cuts, Anela Grgorova, Francesco G. B.De Natale, Charle Dagl, and Thomas S. Huang. Content based mage retreval by Feature Adaptaton and Relevance Feedback, IEEE Trans. On Multmeda, Oct X.S. Zhou,., and Huang, T.S, Relevance feedback n mage retreval: a comprehensve revew, Multmeda System, Ioanns Kompatsars, Mchael G. Strntzs, An Ontology Approach to Object-Based Image Retreval, ICIP, IEEE Gaurav Aggarwal, Ashwn T. V, Sugata Ghosal, An Image Retreval System Wth Automatc Query Modfcaton, IEEE Transactons on Multmeda, June 2002