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The 8th International Conference onComputer Science & Education (ICCSE 2013)April 26-28, 2013. Colombo, Sri Lanka SuC1.5

A NOVEL APPROACH FOR CONTENT BASEDIMAGE RETRIEVAL USING HYBRID FILTER

TECHNIQUES

M S nohnAssociate Professor and Head

PG Department of Computer ScienceKongu Arts and Science Clege

Eroe, Tamil Nadu, Indi - 38 EMil: mnomhi@yhoo.com

tt In general the users are in need to retrieveimages from a collection of database images from variety of

domains. In earlier phase this need was satised by

retrieving the relevant images from different database

simply. Where there is a bottleneck that the images

retrieved was not relevant much to the user query because

the images were not retrieved based on content where

another drawback is that the manual searching time is

increased. To avoid this (CBIR) is developed it is a

technique for retrieving images on the basis of

auomaically -deived feaues such as colou, exue and

shape of images. To provide a best result in proposed work

we are implementing high level ltering where we are

using the Anisotropic Morphological Filters, hierarchical

Kaman lter and particle lter proceeding with feature

extraction method based on color and gray level feature

and aer this the results were normalized.

K -- Content-Based Image Retrieval (CBIR),Anisotropic Morphological Filters, hierarchical aman lter

and particle lter, mahalanobis distance, Color feature

extraction, Gray-level extraction.

NTRODUCTON

ContentBased mage Retrieval (CBR) is also denoted asQuery By mage Content (QBC) and ContentBased Visual

nformation Retrieval (CBVR) Searching for digitalimages in large databases is a big problem which is theimage retrieval problem is solved with the help of CBRFrom the name itself Contentbased is that the search willanalyze the actual contents of the image n CBR system'content denotes the contet that refers colors, shapes,tetures etc Without the keyword we are not ability toeamine image content n this system the features areetracted for both the database images and query images nCBR each image that is stored in the database has itsfeatures etracted and compared to the features of the queryimage n general the CBR system has undergone two

steps First is feature Etraction which is a process to etractthe images features based on color, teture, shape etc to adistinguishable etent Second is matching these featuresresults obtained from rst step to yield visually similar

978-1-4673-4463-0/13/$3100 013 I 518

D S hppnAssociate Professor

Dertet fComputer ScienceErode Arts an Science College

Erode, Tamil Nadu, Indi - 38 EMil: vishppn@yhoo.co.in

results The content based image retrieval (CBR) systemhaving the purpose is to allow users to retrieve relevantimages from large image repositories or database n CBRan image is usually represented as a set of low leveldescriptors from that a series of underlying similarity ordistance calculations are implemented to effectively dealwith the different types of queries Usually independentrepresentations in an attempt to induce high level semanticsfrom the low level descriptors of the images is done with thecalculation of distances or scores from different images

n earlier system they had eposed that using a singleregion

query eample is superior than using the entire image as thequery eample However, the multipleregion queryeamples outperformed the singleregion query eample andalso the wholeimage eample queries [] A novel manifoldlearning algorithm [2], identied GR, for image retrievaln the primary step, they built amongclass nearest neighborgraph and a withinclass nearest neighbor graph to moldboth geometrical and discriminate structures in the guresThe ordinary spectral practice was then employed to nd amost favorable projection, which high opinions the graphstructure This method, the Euclidean distances in thecondensed subspace can reproduce the semantic structure in

the data to a little amount [3] This epanded three kinds ofDistance Metric Measures in the vein of Euclidean Distance,ChiSquare Distance and Weighted Euclidean Distance orfeature matching progression are employed Thefundamental thought at the back CBR is as soon as buildingan image database, feature vectors as of images like color,teture are to be etracted and storing the vectors which isnot enlarged at this point

Michael Lam [5] in which Gabor and Markov descriptorscarry out similarly, but Gabor features are have a preferencesince they are more rapidly to determine and contrastUnfortunately, the ratings used in lung nodule annotation do

not seem to be consistent, and this poses an unsolvedproblem for contentbased image retrieval assessmentFazalemalik [] A CBR algorithm was proposed which is

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based n the clr histgram using Laplacian lter t reducethe nise and prvides an enhanced image with mre detailinfrmatin but the accuracy f the system is lacked ChiaHung Wei [] launched a cntentbased apprach t medicalimage retrieval Fundamentals f the key cmpnents f

cntent based image retrieval systems are initiated [8]Suggested a methd t cmbine a given set f dissimilaritynctins Fr each similarity functin, a prbabilitydistributin is built Assuming statistical independence,these are made use f t design a new similarity measurewhich cmbines the results btained with each independentfunctin [15] n the paper image retrieval methd usingOverlap Fusin CBR technique was cmpared with NnOverlap Fusin CBR technique

n eisting system there are three main cncepts was dneFirst is ltering prcess where three ltering technlgies

were used Secnd cncept is edge detectin mechanism andthird is crner detectin mechanism At last additinallythinning ltering methd is implemented and with that theptical w mechanism is dne fr identiing the directinand magnitude changes The rst step f ltering prcesswhich is als referred as "smthing is used fr reducingthe nise t imprve the quality f image Fr the lteringprcess they have used Mean, Median and Gaussianltering technlgy n mean lter als referred as averagelter which is used fr eliminating the nise by perfrmingspatial ltering methd n each individual piel in animage Fr eample cnsider the 3 x3 lter windw is asfllws:

. P4 Ps P J teredpixel = (PI + P2+'" +pg ) /9

'The abve step can be frmulated as

T F x in l wi b x

The secnd is median ltering technlgy which isnnlinear tl while the mean ltering is linear tl Thesetw is differing by very little that is median lter remvesnise whereas the mean lter just spreads it arund evenlyThe median ltering is perfrming better than the averageltering in the sense f remving impulse nise But in thisltering methd it remves bth the nise and the nedetails als Third ltering technlgy is Gaussian lterwhich is similar t the median lter but in Gaussian ltercan be achieved at ed cst per piel independent f ltersize Aer the ltering prcess edge detectin is dne whichis fr etracting the imprtant feature frm the images Andthe crner detectin is dne fr nding the crrespndingpints acrss multiple pints Aer these methds thinninglter is used t imprve the feature etractin prcess Butin this wrk while nise remval prcess the lteringtechniques can als remve the ne detail with the nise, s

the quality f image will be degraded

n ur research we are implementing the cncept f CBRusing the high level ltering methds and feature etractin

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prcess The ltering is dne by using tw lteringtechniques called hierarchical Kaman lter and particle lterand Anistrpic Mrphlgical Filters n AnistrpicMrphlgical Filters are used n binary t lter the niseeffectively The hierarchical Kaman lter in which it

eliminates the glbal linear gure and the particle lterhandle the lcal nnlinear gure Aer this the featureetractin is based n clr feature and gray level featureBy quantifying the HSV clr space int nnequal intervalsthe clr f each subblck is etracted The clr feature srepresented by cumulative clr histgram Teture f eachne subblck is attained by using gray level cccurrencematri Aer these tw prcesses the nrmalizatin prcessis eecuted t make the cmpnents f feature vectr equalimprtance The similarity measurement frm thesebservatins is nished based n mahalanbis distancewhich is t measure distance fr every bservatin With

this result the images will be retrieved frm the databaseimages based n the query image

The main cntributin f wrk is that: Cllecting the imagecllectin and maintain in a database and train thse imagesby applying the ltering and feature etractin methdswhich can be briey eplain belw Get the user queryimage and test the image with the same ltering and featureetractin techniques Stre all the bservatins andnrmalize the result Find the distance fr every bservatinfrm that nd the similarity between the bservatinsPrduce the cntent based image cllectins with respect tthe user query image

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This paper rganized as fllws in sectin 2 the intrductinf DBR system was described n sectin 3 the Filteringtechniques f prpsed wrk is eplained n sectin 4 thefeature etractin prcess with the mahalanbis distancemeasure is described Finally in sectin 5 the eperimental

results are illustrated

T NTRODUCTON OF CBR SYSTEM

The Cntentbased image retrieval (CBR) is as well knwnas query by image cntent (QBC) plus cntentbased visualinfrmatin retrieval (CBVR) is the purpse f cmputerapparitin t the image rescue prblem, t be eact, theprblem f searching fr relevant images in large databasesCntentbased represents the lk fr will eamine thegenuine cntents f the image The name cntent in thisperspective may pass n clrs, shapes, tetures, r anyther infrmatin that preserve be riginated frm the imageitself Eclusive f the talent t eamine image cntent,searches are bliged t rely n metadata fr instancedescriptins r keywrds, which may pssibly be labriusr cstly t generate Query by illustratin is a queryprcedure with the aim f engages prviding the CBRsystem by means f an eample image that it will then baseits search in the lead The riginal search algrithms maycntrast depending n the applicatin, nevertheless resultimages are suppsed t every single ne share widespreadelements with the ffered mdel Optins fr prvidingeemplar images t the methd take accunt f:

A pre btainable image may be supplied by the user rpreferred as f a indiscriminate set

The user sketches a bumpy apprimatin f the imagethey are lking fr, fr instance with spts f clr rgeneral shapes

This query technique eliminates the cmplees that cancme up when trying t epress images by means fwrds

CBR systems can als make use f relevance feedback,where the user prgressively renes the search results bymarking images in the results as relevant, nt relevant,

r neutral t the search query, then repeating the searchwith the new infrmatin The cntent cmparisntechnique is general methd in favr f etracting cntentfrm images cnsequently that they can be withutdiculty cmpared The methds sketched are nt denitet in the least eacting applicatin dmain nvestigativeimages based n the clrs they cntain are ne f thelargest part etensively used techniques since it des ntdepend n image size r directin Clr searches willfrequently engage cmparing clr histgrams, despite thefact that this is nt the anymre than technique in practiceTeture measures search fr image patterns in images and

hw they are spatially dened

Tetures are represented by teels which are then placedint an amunt f sets, depending n hw many tetures are

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discvered in the image These sets nt nly characterize theteture, ther than als smewhere in the image the tetureis placed Shape des nt pass n t the shape f an imagebut t the shape f an eacting regin that is being requiredepsed Shapes will en be determined riginal applying

segmentatin r edge detectin t an image n sme casesperfect shape detectin will have need f humaninterventin because methds like segmentatin are verydicult t cmpletely cmputerize

Queryimage

Queryimagefeatures

Similaritymeasurement

Relevantimageresults

Imagecollection

Featuredatabase

Fig 2 Architecture ofCBTR system

FLTENG TECHNQUES OF PROPOSED WO K

When an image is btained by a camera r ther imagingsystem, frequently the visin system fr which it is prpsedis unable t use it directly The image may be crrupted byaccidental variatins in intensity, variatins in illuminatin,r pr cntrast that must be cntracted with in the earlyphases f visin prcessing n general in image prcessingthe ltering mechanism is used t smthing the image rused t remve the nise frm the image n ur wrk weare using the Anistrpic Mrphlgical Filters andHierarchical Kaman lter and particle lter t prvide thebetter denised image fr further prcess This sectin iseplained briey belw

A. Anisotropic Morphological Filters

This lter is based n the shape and rientatin fstructuring element at each piel The rientatin isspecied by means f a diffusin prcedure f the averagesquare gradient eld, which nrmalizes and make bigger therientatin infrmatin frm the edges f the things t thehmgeneus areas f the image; and the shape f the

rientated structuring elements can be linear r it can begiven by the space t relevant edges f the bjects Theprpsed Anistrpic Mrphlgical lters are emplyed nbinary and graylevel images fr imprvement f

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anistrpic features such as cherent, wlikearrangements We sptlight n discrete mrphlgy whererientated structuring cmpnents vary ver the spaceaccrding t a vectr eld

We describe rbust infrmatin n the structuring elementsfrm an impenetrable and regular directin eld fund frmthe image The discrete mrphlgical prcessing is, thus,regularly and lcally adapted t sme features that alreadyare present in the image, but that this prcessing aspires thighlight The creativity f ur apprach is that thespatiallyvariant anistrpic numerical penings/ clsingsare cmputed frm their direct algebraic characterizatinsAdvanced lters (based upn successive penings andclsings) can be then used fr augmentatin f anistrpicimages features such as cherent, wlike structures t isals imprtant t bservatin that the rientatin eld is

calculated nce frm the riginal image, and then, fr handsinvlving sequential penings/clsings the matchingrientatin eld is used This is cherent with the mrerecent theretical results n adaptive mathematicalmrphlgy

B. Overview o Hierarchical Kalman and Particle Filtern mst image retrieval cases, target bjects are beneathnatural images that can be cnsidered as a linear Gaussianimage feature in glbal view, and nnlinear, nnGaussiangure is limited in a lcal regin Even fr sme unepectedgure, it still can be regarded as smth gure with high

enugh sampling frequency The Kalman lter is an ptimalestimatin methd fr linear Gaussian gure Cnsequently,fr glbal view, the Kalman lter can helplly estimateglbal gure characteristics, eg, the psitin, velcity, andacceleratin f gure in carse level With these gurecharacteristics, it is reasnable t frecast the bjectarrangement at the net structure, which prvides a carseslutin t the tracking prblem n the lcal view, becausef the nnlinear, nnGaussian gure, the result f carseestimatin may nt be the grund fact Hwever, in thecnstraint f natural gure, it shuld nt be etreme awayfrm the grund truth As a result, the particle lter is

adpted in lcal (ne) estimatin fr achieving a neslutin

Spntaneusly, hierarchical inference is cmpiled f carseevaluatin part, regin estimatin sectin, and neestimatin part Kalman lter is the heart f the carseestimatin and apprimates glbal bject gure Theregin estimatin cmpnent becmes aware f thealteratin f glbal gure and engenders the prpsalallcatin f the particle lter The ne estimatinultimately estimates the perfect bject lcality and it is tthe utput f the whle tracking algrithm n additin, the

utput f ne estimatin feeds back int carse estimatinas an bservatin f the Kalman lter

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V TH E F EATU E EXTRACTON PROCESS WTHMA HA LANOBS DSTANCE M EASU E

n this sectin aer the ltering prcess the featureetractin is dne using clr and gray level features and thebservatin f the result will be stred in database This

prcess is eecuted bth testing phase and training phase ntraining phase the cllectin f images will be stred in adatabase Based n this features are etracted and stred indatabase n testing phase als the same prcess is dne andthe bservatin f feature etractin is dne fr net prcessf similarity measurement The similarity is dne usingMahalanbis distance fr each f the bservatins

A.Color Feature Extraction

The challenge f semantic gap between the lwlevel visualfeatures and the highlevel semantic cncepts is ccurrenceby Cntentbased image retrieval (CBR) system t wuldbe valuable t make CBR arrangements which maintainhighlevel semantic query The majr prpsal is tincrprate the strengths f cntent and keywrdbasedimage indeing and retrieval algrithms at the same time asease their individual diculties The CBR fundamentallyperfrms tw main tasks; at the utset feature etractin,which is etracting feature psitin frm the query imagewhich is in general knwn as feature vectrs r elsesignatures f the image which perfectly characterizes thesubstance f each image in the database

They are very smaller in dimensin aer thse riginal

vectrs The subsequent task is similarity measurement,which basically determines the detachment between thequery image and each image in database using the cmputedfeature vectrs and thus recvers the clsest match/matchesCntent based mage retrieval is an ecient methd t lkfr frm beginning t end an image database thrugh imagefeatures such as clur, teture, shape, pattern r anycmbinatins f them Clur is an imprtant cue fr imageretrieval The clur based features image retrieval hasprved successful fr a huge image database, hwever it isabandned that clur is nt a cnstant parameter, as itdepends nt nly n surface characteristics but als

capturing cnditins such as enlightenment, the devicecharacteristics pint f view f the device T retrievedesired images, user has t make available a query imageThe system then perfrms certain feature eractinprcedures n it and crrespnds t it in the frm f featurevectrs The similarities distances between the featurevectrs f the query image and thse f the images in thedatabase are then calculated and retrieval is perfrmed Freach image in the image database, its features are etractedand the btained feature space (r vectr) is stred in thefeature database When a query image cmes in, its featurespace will be cmpared with thse in the feature database

ne by ne and the similar images with the smallest featuredistance will be retrieved

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Stepladders f the algrithm are specied under

Step1: Three clr planes namely Red, Green and Blue areseparatedStep2: Fr every image surface rw mean and clumn meanf clrs are calculatedStep3: The average f all rw means and all clumns meansis calculated fr each imageStep4: The features f all 3 planes are cmbined t frm afeature vectr Once the feature vectrs are generated fr allimages in the database, they are stred in a feature databaseStep: The Mahalanbis distances between the featurevectr f query image and the feature database arecalculated using Eq given belw

/ = { (XI - }/) - (XI - jrJThe value f d is calculated by summatin f the squares fdifference f the features f database image and query

image as mentined in Eq (I Lwer the value f E i} inabve Eq pint t higher relevance t the query image

B. Gray Level Feature Extraction

Gray level cccurrence technique apply greylevel cccurrence matri t mckup statistically the apprachassured greylevels ccur in relative t ther greylevelsGreylevel matri is a mld whse cmpnents calculate thequalied frequencies f ccurrence f grey levelarrangements amng pairs f piels by means f a speciedspatial relatinship The thery f grey level cccurrencematri is eplained belw Given an image O,j assent t

be lcatin peratr, and A is a N X matri whseelement is the number f times that pints with grey level(intensity) O ccur, in the psitin specied by therelatinship peratr relative t pints with grey level ). Let P be the X N matri A that is prduced bydividing with the ttal number f pint pairs that satisfy pP} is a measure f the jint prbability that a pair fpints satisfying p will have values l g) is called a cccurrence matri dened by p The relatinship hand isdened by an angle e and distance d.

C Similarity measurement using Mahalanobis Distance

Mahalanbis distance is als called quadratic distancet measures the separatin f tw grups f bjects Suppse

we have tw grups with means and variati Xj,Mahalanbis distance is given as fllws: d ij " r1 - }) - I - j2 e data f the twgrups must have the same number f variables (the samenumber f clumns) but nt necessarily t have the samenumber f data (each grup may have different number frws)

V ESULTS AND DSCUSSON

n ur eperiment rst the query image is gt frm the userThen the prcess f ltering and clr feature etractin

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will be dne At the end f result will be retrieved frm thecllectin f database based n the input query image usingthe distance calculatin and similarity matching These stepswill be shwn as gures as fllws

) Quy u i dit : nt D tp nd p

Fig 3: Input query image

Fig 4Color feature extarction

) Rv mag EJe Jew se ools Qeso oV el

Fig 5 Rerieved iamge

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A. Precision vs. Number o imagesThis graph shws the precisin rate f eisting and prpsedsystem based n tw parameters f precisin and thenumber f images Frm the graph we can see that, whenthe number f number f images is advanced the precisinals develped in prpsed system but when the number fnumber f images is imprved the precisin is reducedsmewhat in eisting system than the prpsed systemFrm this graph we can say that the precisin f prpsedsystem is increased which will be the best ne The valuesare given in Table 1

able I Precision vs. Number of images

O o. f Prosd Exisngim ges sysm sym 0 .30 .21

2 20 .52 .453 30 .63 .5 0 .69 .5 50 .75 .4

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Fig 6 Precision vs. Number of images

n this graph we have chsen tw parameters called numberf images and precisin which is help t analyze the eistingsystem and prpsed systems The precisin parameter willbe the Y ais and the number f images parameter will bethe X ais The blue line represents the eisting system and

the red line represents the prpsed system Frm this graphwe see the precisin f the prpsed system is higher thanthe eisting system Thrugh this we can cnclude that theprpsed system has the effective precisin rate

B. Recall vs. Number of images

This graph shws the recall rate f eisting and prpsedsystem based n tw parameters f recall and number fimages Frm the graph we can see that, when the numberf number f images is imprved the recall rate alsimprved in prpsed system but when the number fnumber f images is imprved the recall rate is reduced ineisting system than the prpsed system Frm this graphwe can say that the recall rate f prpsed system isincreased which will be the best ne

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able 2 Recall vs. Number of images

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Fig 7 Recall vs. Number of images

n this graph we have chsen tw parameters called numberf images and recall which is help t analyze the eistingsystem and prpsed systems n the basis f recall n Xais the iteratin parameter has been taken and in Y aisrecall parameter has been taken Frm this graph we seethe recall rate f the prpsed system is in peak than the

eisting system Thrugh this we can cnclude that theprpsed system has the effective recall

C. F-measure vs. Number of images

This graph shws the Fmeasure rate f eisting andprpsed system based n tw parameters f Fmeasure andnumber f images Frm the graph we can see that, whenthe number f number f images is imprved the Fmeasurerate als imprved in prpsed system but when the numberf number f images is imprved the Fmeasure rate isreduced in eisting system than the prpsed system Frmthis graph we can say that the Fmeasure rate f prpsed

system is increased which will be the best ne The valuesf this Fmeasure rate are given belw

able 3: F-measure vs. Number of images

S o. f ages ropoe Extngte te1 10 08 0.762 20 082 0.73 30 04 0.624 40 05 0.525 50 054 0.2

0 042 0.32

The Fmeasure and the number f images are shwn in thegure 8 This graph shws the Fmeasure rate f the

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eisting and prpsed systems based n tw parametersFrm the graph we can see that, when the number f numberf images is imprved the Fmeasure rate als imprved inprpsed system but when the number f number f imagesis imprved the recall rate is reduced in eisting system than

the prpsed system Frm this graph we can say that therecall rate f prpsed system is increased which will be thebest ne

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