B.G.Prasad, K.K.Biswas and S.K.Gupta Department of Computer …bgprasad/ivcnz-color.pdf · 2002....
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B.G.Prasad, K.K.Biswas and S.K.Gupta Department of Computer Science & Engineering Indian Institute of Technology, New Delhi, INDIA bgprasad,kkb,skg @cse.iitd.ernet.in In this paper we present a technique to retrieve images by region matching on color, shape and location. We extract up to three dom- inant regions in the image using color clusters in RGB space. Each region is indexed with integrated color, shape and location features as well as various combinations of region features are also indexed. The resulting indices and their related metadata are stored in a Hash structure. Images are retrieved based on the color, shape and loca- tion similarity with that of the queried image. The shape feature is normalized to make it invariant to translation, rotation and scaling. The retrieval process is non-cascading and images can be retrieved based on color or shape or location or based on a combined color- shape or color-shape-location index. Results obtained show that the retrieval effectiveness increases in non-cascaded region-based querying by combined color-shape-location index, compared to our earlier cascaded retrieval method based on color followed by shape matching. Keywords: Image indexing, Content-based image retrieval, Color- shape-location index There has been a lot of interest in content-based image retrieval (CBIR) using visual features over the last decade. An overview of the work can be found in [Goodrum 2000][Gudivada and Ragha- van 1995][Marsicoi et al. 1997][Rui et al. 1999]. Due to the large dependence on the use of low-level visual features, there is scope for automated and efficient solutions to this problem. Content- based image retrieval is like an information filter process and hence should provide a high percentage of relevant images for the user upon querying. It should conform to human perception of visual semantics. In general, image features tend to capture only one of many aspects of image similarity and hence it becomes difficult to clearly specify what or how a user should initiate queries. The pres- ence of large volumes of digital repositories leads to many schemes of indexing and retrieval of such data (e.g. QBIC [Flickner et al. 1995], Netra [Ma and Manjunath 1997], VisualSEEk [Smith and Chang 1996], etc). In all these cases, the user is interested in seek- ing the images similar to his query. Color is one of the most important image indexing features employed in CBIR [Flickner et al. 1995][Ma and Manjunath 1997][Ravishankar et al. 1999][Smith and Chang 1996]. A per- ceptual technique based on color cardinality is dealt in [Androutsos et al. 1999]. Binary signatures using bit string representation for color feature is used in [Chitkara et al. 2000]. A grid is superim- posed on the entire image to obtain partition-based image retrieval, but is not rotation invariant. No access structure is used to support the signature-based representation of color. The proposal of using variable number of color histograms for color representation, de- pending on number of colors in the image, is discussed in [Stehling et al. 2000]. It is shown to be better than using global histograms. In [Ko et al. 2000], a flexible sub-block image retrieval algorithm robust to translation, lighting change and object appearance is pro- posed. They use a reduced color space to overcome the problems of global color histograms. Due to the limitations of using color alone as a global feature, usually other features like shape, texture and spatial location are added to the feature space to enhance the retrieval efficiency and effectiveness. Also, the global color-based methods suffer from problems of non-invariance and large storage requirements. Shape is an important feature for perceptual object recogni- tion and classification of images. Many techniques such as chain code, polygonal approximations, curvature, fourier descriptors, grid-region based, radii method and moment descriptors have been proposed and used in various applications. A region-based shape representation and indexing scheme that is translation, rotation and scale invariant is proposed in [Lu and Sajjanhar 1999]. Compared to Fourier method, it is shown to give better retrieval performance but works only on binary images. Shape-based indexing of color images is proposed in [Prasad et al. 2001b] that also uses similar shape representation but different indexing and similarity measures so as to obtain better retrieval results for color image regions. Fur- ther, along with color, shape has been used as an additional fea- ture to index the extracted regions within the images. The query is sequentially cascaded first by color and then by shape [Prasad et al. 2001a], which produces better results compared to matching by color or shape alone. In this paper we propose to use region-wise location information additionally to index images to enable image retrieval based on spa- tial location of regions. It also allows for matching images based on relative location positions with respect to regions within an image. To compute location index, we divide the image space into 3x3 grid cells and number them 1 to 9. Depending on the cell number that is maximally covered by a region, it is allocated an index equal to the cell number. This is then combined with the color and shape indices to provide an integrated color-shape-location index allow- ing for a non-cascaded matching on these combined features. The composite indexing method is simple, computationally less inten- sive and shown to perform better. The paper is organized as follows. In the next section, we briefly explain the feature extraction and image representation approach adopted. Section 3 describes in detail the indexing methodology used to index images based on individual as well as the combined color-shape-location features. The Hash data structure and similar- ity matching criteria used are explained in section 4. In section 5, we depict the experimental results and performance evaluation of our system. Conclusions about the employed scheme are discussed in section 6. Our indexing approach employs color clustering that is carried out in RGB-space to extract up to three dominant regions in images based on human perceptual colors [Ravishankar et al. 1999]. It
Transcript of B.G.Prasad, K.K.Biswas and S.K.Gupta Department of Computer …bgprasad/ivcnz-color.pdf · 2002....
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B.G.Prasad,K.K.BiswasandS.K.GuptaDepartmentof ComputerScience& Engineering
IndianInstituteof Technology, New Delhi, INDIA7bgprasad,kkb,skg8 @cse.iitd.ernet.in
9;:=A@CBED�>In this paperwe presenta techniqueto retrieve imagesby regionmatchingoncolor, shapeandlocation.Weextractup to threedom-inantregionsin theimageusingcolor clustersin RGB space.Eachregion is indexedwith integratedcolor, shapeandlocationfeaturesaswell asvariouscombinationsof region featuresarealsoindexed.Theresultingindicesandtheirrelatedmetadataarestoredin aHashstructure.Imagesareretrievedbasedon thecolor, shapeandloca-tion similarity with thatof thequeriedimage.Theshapefeatureisnormalizedto make it invariantto translation,rotationandscaling.Theretrieval processis non-cascading andimagescanberetrievedbasedon color or shapeor locationor basedon a combinedcolor-shapeor color-shape-locationindex. Resultsobtainedshow thatthe retrieval effectiveness increasesin non-cascaded region-basedqueryingby combinedcolor-shape-locationindex, comparedto ourearliercascadedretrieval methodbasedoncolor followedby shapematching.
Keywords: Imageindexing,Content-basedimageretrieval,Color-shape-locationindex
F GIH >A@CJ�K�L=D=>NMOJ HTherehasbeena lot of interestin content-basedimageretrieval(CBIR) usingvisual featuresover the lastdecade. An overview ofthe work canbe found in [Goodrum2000][GudivadaandRagha-van1995][Marsicoiet al. 1997][Ruiet al. 1999]. Due to the largedependenceon the useof low-level visual features,thereis scopefor automatedand efficient solutionsto this problem. Content-basedimageretrieval is likeaninformationfilter processandhenceshouldprovide a high percentage of relevant imagesfor the useruponquerying. It shouldconformto humanperceptionof visualsemantics.In general,imagefeaturestendto captureonly oneofmany aspectsof imagesimilarity andhenceit becomesdifficult toclearlyspecifywhator how ausershouldinitiatequeries.Thepres-enceof largevolumesof digital repositoriesleadsto many schemesof indexing andretrieval of suchdata(e.g. QBIC [Flickner et al.1995], Netra [Ma andManjunath1997], VisualSEEk[Smith andChang1996],etc). In all thesecases,theuseris interestedin seek-ing theimagessimilar to hisquery.
Color is one of the most important image indexing featuresemployed in CBIR [Flickner et al. 1995][Ma and Manjunath1997][Ravishankaret al. 1999][Smithand Chang1996]. A per-ceptualtechniquebasedoncolorcardinalityis dealtin [Androutsoset al. 1999]. Binary signaturesusingbit string representationforcolor featureis usedin [Chitkaraet al. 2000]. A grid is superim-posedon theentireimageto obtainpartition-basedimageretrieval,but is not rotationinvariant. No accessstructureis usedto supportthesignature-basedrepresentationof color. Theproposalof usingvariablenumberof color histogramsfor color representation,de-pendingonnumberof colorsin theimage,is discussedin [Stehlinget al. 2000]. It is shown to bebetterthanusingglobalhistograms.In [Ko et al. 2000],a flexible sub-blockimageretrieval algorithm
robustto translation,lighting change andobjectappearanceis pro-posed.They usea reducedcolor spaceto overcomethe problemsof global color histograms.Due to the limitations of usingcoloraloneasa global feature,usuallyotherfeatureslike shape,textureandspatiallocationareaddedto the featurespaceto enhancetheretrieval efficiency andeffectiveness.Also, theglobalcolor-basedmethodssuffer from problemsof non-invarianceandlargestoragerequirements.
Shapeis an important feature for perceptual object recogni-tion andclassificationof images.Many techniques suchaschaincode, polygonal approximations,curvature, fourier descriptors,grid-region based,radii methodandmomentdescriptorshave beenproposedandusedin variousapplications. A region-basedshaperepresentationandindexing schemethatis translation,rotationandscaleinvariantis proposedin [Lu andSajjanhar1999]. Comparedto Fouriermethod,it is shown to give betterretrieval performancebut works only on binary images. Shape-basedindexing of colorimagesis proposedin [Prasadet al. 2001b] that alsousessimilarshaperepresentationbut differentindexing andsimilarity measuressoasto obtainbetterretrieval resultsfor color imageregions.Fur-ther, along with color, shapehasbeenusedas an additionalfea-ture to index the extractedregionswithin the images. The queryis sequentiallycascadedfirst by color and thenby shape[Prasadet al. 2001a],which producesbetterresultscomparedto matchingby coloror shapealone.
In thispaperweproposeto useregion-wiselocationinformationadditionallyto index imagesto enableimageretrieval basedonspa-tial locationof regions.It alsoallowsfor matchingimagesbasedonrelative locationpositionswith respectto regionswithin animage.
To computelocationindex, we divide the imagespaceinto 3x3grid cellsandnumberthem1 to 9. Dependingon thecell numberthatis maximallycoveredby aregion, it is allocatedanindex equalto thecell number. This is thencombinedwith thecolor andshapeindicesto provide an integratedcolor-shape-locationindex allow-ing for a non-cascadedmatchingon thesecombinedfeatures.Thecompositeindexing methodis simple,computationallylessinten-siveandshown to performbetter.
Thepaperis organizedasfollows. In thenext section,webrieflyexplain the featureextraction and imagerepresentationapproachadopted. Section3 describesin detail the indexing methodologyusedto index imagesbasedon individual aswell asthecombinedcolor-shape-locationfeatures.TheHashdatastructureandsimilar-ity matchingcriteriausedareexplainedin section4. In section5,we depict the experimental resultsandperformanceevaluation ofoursystem.Conclusionsabouttheemployedschemearediscussedin section6.
P Q+R BE>AL0@ RTS�U >A@CB3D=>NMOJ H B H K G�V BEW RYXZR�[ @ R < R�H=\>NBE>NMOJ H
Our indexing approachemploys color clusteringthat is carriedoutin RGB-spaceto extract up to threedominantregions in imagesbasedon humanperceptual colors [Ravishankaret al. 1999]. It
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hasbeenshown that the cardinalityof imagesby color is four ontheav] erage[Stehlinget al. 2000]. We resortto a weaksegmenta-tion, sinceimageretrieval doesnotentailsolvingthegeneralimagerecognitionproblem. It is sufficient thata retrieval systempresentsimilar imagesin someuser-definedsense.
To segmentimagesbasedon dominantcolors,a color quantiza-tion in RGB-spaceusing25perceptualcolorcategoriesis employed[Ravishankaret al. 1999]. Fromthesegmentedimageregions,wefind theenclosingminimumboundingrectangle(MBR) of eachre-gion, its location,area,color information,numberof regions,etc.All theseare storedin a metafileand usedfor matchingimagesbasedoncolor.
For eachof thedominantregionswecomputethemajoraxis,mi-nor axisandthecentroid.It is thennormalizedsuchthatthemajoraxis is parallelwith the horizontalaxis andthe width occupies96pixels. Thena grid of 96x96pixels is placedover the normalizedshaperegionwith thecentroidof theregioncoincidingwith thecen-terof thegrid. Thegrid is dividedinto 8x8cells(eachof size12x12pixels). Depending on coverageof thepixelswithin eachcell, it isassigneda binary value of 1 or 0. Thus the imageregion trans-latesinto a 64 bit uniquesequenceof 1’s and0’s. Theeccentricity(measuredin termsof thenumberof rows the region occupies)ofthe imageregion is usedto index theshapeandconstructthehashstructurebasedon color andshape.Thenwhile matching,thecov-eragealong rows and columnsare usedto pruneout non-similarregionsbasedonsomethresholdvalue.
For locationindexing, the imagespaceis divided into 3x3 gridnumbered1 to 9. The region is likely to overlapnumberof cellsin the imagespace.The index assignedis the cell numberthat ismaximally coveredby the region. If a region overlapsmorethanonegrid cell, thenthecentralareaof theregionis usedto determinethelocationindex. Theclassificationis accordingto a locationmapcontaining9 regionsof theimagespaceasshown in Figure1.
Figure1: Illustrationof Find location.
A programsegmenthighlighting the find locationprocedureisgiven below. We have consideredimagesizeof 192 x 128 pixelsin our work. The four intersectingcornersof the 9 sub locationsdependingon imagesizeareinitializedat thebeginningof thepro-gram.
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° G�H > R W�@�BE> R K±D=J² J³@ \ NMµJ H M H K R+U¶3·O¸ ¹³º»¼º}½�¾¼¿AÀAÁrÂThe color spaceis groupedinto 25 perceptualcolor categoriesin-dexed as 1,2,3, ... 25. The imageis segmentedand n dominantregionsareselected.Theoverall color index is formedasfollows:
CI Ã ∑niÄ 125n Å iCiwhereCi is theindex of the i
th region. Imageswith similar overallcolor indicesarestoredin thesamehashentryof hashindex struc-ture.Along with it, othermetadataabouttheregionarealsostored.For our studywe have consideredonly thefirst threedominantre-gions.
¶3· Æ Ç+ÈAÉ}Ê=Á¾¼¿AÀAÁrÂTheshapecategoriesSi rangefrom 1 through8 basedoneccentric-ity of thedominantregions.Theshapeindex for n dominantregionsis formedasfollows:
SI à ∑niÄ 18n Å iSiwhereS1 Ë S2 Ë S3 ˧ÌÍÌ¼Ì aretheshapeindicesof thedominantregionsindecreasingorderof size(S1 is themostdominantregion).
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¶E· ¶ Îcº�ÏcÉ}Ði¾¼º¿2¾Ñ¿AÀÁrÂIt is usually desiredto retrieve imagesbasedon regions/objectspresentat particular spatial locations within the image space.Hence,we determinethe grid cell that the extractedimageregionmaximally covers in the imagespaceand assignthe correspond-ing cell numberasits locationindex. Theoverall locationindex isformedasfollows:
LI à ∑niÄ 19n Å iLiwhereL1 Ë L2 Ë L3 Ë§Ì¼Ì¼Ì arethelocationindicesof thedominantregionsin decreasingorderof size.
¶E· Ò ¹³ºÓ Ê=ºÔo¾¼ÐIÁÏNº»Ñºr½CÕCÔoÈAÉ}Ê=Á¾¼¿AÀAÁrÂCascadedretrieval of images,indexing first by color andthenbyshape,resultsin someof the imagesnot beingretrieved from thedatabase.Soa uniqueindex for imageregionsbasedon compositecolor and shapeis proposed. The compositeindex is formed asfollows:
CSI à ∑niÄ 1 Ö 25n Å iCi ×rØ 210 Ù Ö 8n Å iSi ×whereC1 Ë C2 Ë C3 Ë§Ì¼Ì¼Ì andS1 Ë S2 Ë S3 ˧ÌÍÌ¼Ì arethecolorandshapeindicesrespectively of the dominantregions in decreasingorder of size.Theabove indexing methodconsiderstheeccentricityof theregionand its homogeneous color category to form the combinedcolor-shapeindex for representationof the imageregions. Imagesarematchedfor shapesimilarity usingtherow andcolumncoverageasthediscriminatingfactorwithin somethreshold.A detailedproce-dureof theabove indexing methodis explainedin our earlierworkoncascaded retrieval by colorandshape[Prasadet al. 2001a].
¶E· Ú ¹³ºÓ Ê=ºÔo¾¼ÐIÁÏNº»Ñºr½CÕCÔoÈAÉ}Ê=ÁrÕC»¼º�ÏcÉ}ÐI¾Ñº¿'¾¼¿AÀAÁrÂThe previous combinedindex canbe extendedto provide an inte-gratedcolor-shape-locationindex to increasethematchingperfor-mance.Theintegratedindex is formedasfollows:
CSLI à ∑niÄ 1 Ö 25n Å iCi ×}Ø 220 Ù Ö 8n Å iSi ×rØ 210 Ù Ö 9n Å iLi ×whereC1 Ë C2 Ë C3 ˧ÌÍÌ¼Ì , S1 Ë S2 Ë S3 Ë§Ì¼Ì¼Ì and L1 Ë L2 Ë L3 Ë§Ì¼Ì¼Ì are the color,shapeandlocationindicesrespectively of thedominantregionsindecreasingorderof size.
Û Ü>cJ³@�BEW R B H K V BE>ND0´=M H WYD0@�MO> R @CMµBÒE·µ¸ ÝÞÉ}ÔoÈ'ÇßÐi½CàAÏNÐiàA½CÁAn imagestructurebasedon hashingtechniqueis usedto storethecombinedindex of the imageregion-wise. At query time, onlythoseimageregions that are in the samehashbucket as thoseofthequeriedimageregionsarecomparedfor similarity, thusreduc-ing thesearchspaceandtime.
A hashstructureis usedto storethekeys of all imagesbelong-ing to the samegroup in separatebuckets. An instanceof hashtableis shown in Figure2. Along with thecompositeimageindex,featuredatalike numberof dominantregions, color information,color index, area,locationindex, shapestring,shapeindex, columncoveragetotalandrow coverage totalof shaperepresentationof in-dividual regionsaswell asfor regionstakenpair wisearestoredasa vector for eachimageentry in the hashtable. Additionally, thepathnameof eachimageis alsostoredin thevector. Eachof theseconstitutesa featurevectorbasedon the combinedfeatures.Themappingof region informationto thehashindex is shown in Figure3 for all combinationsof regionswithin theimage.
An illustrationof index formationfor a singleregion is giveninthefollowing example:
Figure2: Instanceof hashtable.
Figure3: Hashindex structurefor compositecolor-shape-locationindex.
áCSL â Index à 16 Ø 220 Ù 8 Ø 210 Ù 1 à 16785409
is thecolor-shape-locationindex for a regionhaving acolor= ’16’,shape= ’8’ andlocation= ’1’. All imageregionshaving a simi-lar compositeindex will bemappedto thehashbucket with value’16785409’. Similar analysisfor imageswith multiple regions isdoneto obtainuniqueindicesfor all combinations consideringupto 3 dominantregionsin agivenimage.
Ò3· Æ Ç+¾ÑÓã¾¼»ÑÉc½C¾ÑÐCäãÓ ÁrÉ}ÔoàA½CÁ åº}½ÔoÈAÉ}Ê=ÁÓãÉ}ÐiÏNÈA¾Ñ¿AæTo find the bestmatchbetweenthe query imageand the imagesindexed to samehashlocation, we make useof row and columncoverageof individualregions.Thesimilarity measureis computedasfollows:
1. Calculatetherow coverageRC j andcolumncoverageCC j ofthe regions in the query imageby countingnumberof cellsalongeachdirection.
2. Calculatetherow coverageRCç j andcolumncoverageCCç j oftheregionsin theimageextractedfrom databaseby countingnumberof cellsalongeachdirection.
3. Find therow andcolumndifferencebetweenqueryimagere-gionsandregionsin the imageselectedfrom databaseusingtheequations:
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Rd à ∑8j Ä 1 è RC j é RCç j è and Cd à ∑8jÄ 1 èCC j é CCç j è4. If Ö Rd Ù Cd ×ê T (asuitablethreshold),thentheimagesmatch.ë S�Uì[ER @�M V¨R�H >cBÞ²�@ R N
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Figure4: Retrieval resultson databaseof flowers,fruits, vegetables andsimulatedfiguresbasedon (a) color (1st col, 1st & 2nd row), (b)shape(2ndcol, 1st& 2ndrow), (c) location(3rd col, 1st& 2ndrow) (d) color-shapeindex (3rd row, left) and(e) color-shape-locationindex(3rd row, right).
Figure5: Retrieval resultson databaseof flagsbasedon (a) color (1stcol, 1st& 2ndrow), (b) shape(2ndcol, 1st& 2ndrow), (c) location(3rdcol, 1st& 2ndrow) (d) color-shapeindex (3rd row, left) and(e) color-shape-locationindex (3rd row, right).
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Figure6: Retrieval resultsoncombineddatabasebasedon (a) color (1stcol, 1st& 2ndrow), (b) shape(2ndcol, 1st& 2ndrow), (c) location(3rdcol, 1st& 2ndrow) (d) color-shapeindex (3rd row, left) and(e) color-shape-locationindex (3rd row, right).
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Figure 7: Comparative graphbetweenfull-image cascadedmatchand region-basednon-cascadedmatchfor database of flowers, fruits,vegetablesandsimulatedfigureson (a)Color (b) Shapeand(c) compositeColor-Shape-locationindex.
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Figure8: Comparativegraphbetweenfull-imagecascadedmatchandregion-basednon-cascaded matchfor databaseof flagson(a)Color (b)Shapeand(c) compositeColor-Shape-locationindex.
