(a)rawRGBimage(b)depth
image
(c)segmentation
labeling
(d)
foregroundness(e)
backgroundness(f)saliency
Fig.3.Overviewofoursaliencymappingalgorithm.RawRGB-Ddata(a-b)isusedtosegmentthesceneintohomogeneousregions,theresultingregionsareshownin(c).Inthenextstep,foregroundandbackgroundmeasuresarecomputedfromcolor,depthandthespatialdistributionoftheregions(d-e).The nalsaliencymapisobtainedbycombiningtheforegroundandbackgroundmaps(f).
regiontothecenteroftheimage.Itwillbelargeforobjectsbeingclosetocornersandsmallforobjectsaroundthecenteroftheimage.
TheBoundaryConnectivitymeasureproposedbyZhuetal.[9]isusedtoquantifyhowheavilyaregionriisconnectedtotheimageboundaries:
len(ri)
BCon(ri)=i(4)
asdepictedinFig.3(c).TwosamplesofforegroundandbackgroundsaliencymapsareshowninFig.3(d)andFig.3(e).Finally,theresultingsaliencymapisshowninFig.3(f).
III.EXPERIMENTALRESULT
Inthissection,weevaluateoursaliencydetectionmethodbyusingtwodifferentdatasets:theRGB-DdatasetprovidedbyCiptadietal.[4],whichweinthefollowingrefertoasDSD(depthsalientdata),andtheMSRAdatasetfromLiuetal.[19].Bothdatasetsincludeimageswithcomplexbackgroundandlowcontrastobjects,aswellasmanuallylabelledgroundtruthmasks(GT)forsalientobjectcandi-dates.Severalstate-of-the-artsaliencydetectionalgorithmsarechosenforcomparisonandareinthefollowingreferredtoasSF[3],MSS[14],IG[13],AC[18],IT[12],MZ[20]andSR[10],respectively.Tobemoreprecise,wecomparetheresultsofdifferentapproachesontheintroduceddatasetsifsaliencymapsareavailableforthecurrentbenchmarkdataset.Soforexample,RGBbasedalgorithmsarenotevaluatedontheRGB-Ddatasetsincetheyarenottailoredtomakeuseoftheadditionaldepthinformation(e.g.AC,SR).Ontheotherhand,weextendedsomeofthealgorithms,e.g.IG[13]toadditionallyutilizedepthinformationifthiswaspossibleinastraightforwardfashion.A.EvaluationonDSDdataset
TheDSDdatasetisanRGB-Ddataset,comprising80RGB-Dimagesusingamobilerobotinareal-worldindoorenvironment.Forperformanceevaluation,we rstgiveavisualcomparisonofdifferentmethodsonthisdataset.ThreeimagesampleresultsontheDSDdatasetareshowninthetopthreerowsofFig.4.The rstcolumnrepresentstheinputRGBimagesamplesandthelastcolumndepictsthebinarygroundtruthmasks.Visually,ourmethod(FBS)performsbestcomparedtoothermethodsinregardtotheGTmasksanddeliversbestresultsinregardtoourdesiredsaliency.Sinceperformanceofsaliencyishighlydependentonthedesiredpropertiesandtheinterpretationofimportantobjectsinscenes,itisgenerallynoteasytocomparedifferentmethods.Therefore,tobeabletoquantifythedifferentresultsoverthewholedatabaseandcomplyingwiththerelatedwork(e.g[3]),wechoosethemeanabsoluteerror(MAE)asameasure,whichsimplydescribesthedifferencebetweentheobtainedsaliencymapSandtheGT.
Conformingtothevisualimpression,Fig.5showsthatourmethodalsooutperformstheotherapproachesinregardto
wherelen(ri)isaregionperimeterontheboundary,area(ri)referstoitsarea.ThesalientregionhasasmallBCon(ri)value,comparedtothebackgroundregion.
Finally,wede neadissimilaritymeasureforbackgroundsaliencybetweenregionsas:BS(ri)=
N j=1
area(ri) area(rj) ·wbs(ri,rj)(5)
ThebackgroundsaliencyBS(ri)maybeinterpretedasthe
differenceofthescaleofregionricomparedtoallotherregionsrj.SimilarlytoFS,theGaussianweightwbs(ri,rj)isde nedas
(BCon(ri)+CDis(ri))2
(6)wbs(ri,rj)=1 exp δbswhicheffectivelydescribesthepositionoftheregion,with
valuesclosetozeroindicatingthattheregionisfarfromcornersandboundaries.Thebackgroundusuallyhasalargervaluethanotherregions.Theparameterδbscontrolstherangeofthebackgroundmeasure.D.FinalSaliencyMap
Becauseofthecomplementaritybetweenforegroundandbackgroundsaliency,wecombinethesetwokindsofsaliencymapstogetherwithdifferentweights.Wenormalizefore-groundFSaswellasbackgroundsaliencyBStotherange[0,1]andassumebothestimationstobeindependent.Hencewecombinetwomeasuresasfollowstocomputethe nalsaliencymapSal,
Sali=BSi·exp( t·FSi)
(7)
Theweighttisdeterminedaccordingtotheinformationcontainedinthecorrespondingmap.Inourcase,wesett=3asthescalingfactorthroughoutallexperiments.
AscanbeseeninFig.3,theinputRGB-Dimages(Fig.3(a)andFig.3(b))are rstmergedintohomogeneousregions