PROD TYPE: COM PP:1 col fig nil PR2195 DTD VER: ED: PrathibaPAGN: Vidya SCAN: global

Richard Nocka

Description

Niveau: Secondaire, Lycée, Terminale
UN CO RR EC TE D PR OO F PROD. TYPE: COM PP:1-12 (col.fig.: nil) PR2195 DTD VER: 5.0.1 ED: PrathibaPAGN: Vidya -- SCAN: global ARTICLE IN PRESS Pattern Recognition ( ) – 1 Semi-supervised statistical region refinement for color image segmentation3 Richard Nocka,?, Frank Nielsenb aGRIMAAG-Département Scientifique Interfacultaire, Université des Antilles-Guyane, Campus de Schoelcher, BP 7209, 97275 Schoelcher,5 Martinique, France bSony Computer Science Laboratories, Inc., 3-14-13 Higashi Gotanda, Shinagawa-Ku, Tokyo 141-0022, Japan7 Received 9 August 2004 Abstract9 Some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. In this paper, we improve and adapt a recent statistical region merging approach to this task, as a non-11 parametric mixture model estimation problem. The approach appears to be attractive both for its theoretical benefits and its experimental results, as slight bias brings dramatic improvements over unbiased approaches on challenging digital pictures.13 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. Keywords: Image segmentation; Semi-supervised grouping15 1. Introduction Grouping is the discovery of intrinsic clusters in data [1].

• algorithm


• regions obtained


• well-known benchmark


• single event's


• grouping


• segmentation


• algorithm probabilistic-sorted image11

Sujets

• Première
• Nielsen
• Algorithm
• Grouping
• Segmentation

Informations

• Publié par: profil-zyan-2012
• Nombre de lectures: 23
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

13335373931434547494

1.Introduction
accuratecandidacy,assegmentationshouldcomeupwith
partition(s)fromacandidatesegmentationset
[3]
.
GroupingisthediscoveryofintrinsicclustersindataWiththeadventofmediamakingiteasierandcheaperto
[1]
.Imagesegmentationisaparticularkindofgroupingin
collectandstoredigitalimages,unconstraineddigitalpho-
whichdataconsistsofanimage,andthetaskistoextracttographicimageshaveraisedthischallengeevenfurther
asregionstheobjectsausermayfindconceptuallydistincttowardsbothcomputationalefficiencyandrobustprocess-
fromeachother.Theautomationandoptimizationofthising.Considerforexamplethewell-knownbenchmarkimage
taskfacecomputationalissues
[2]
andanimportantcon-
lena
in
Fig.1
.Userswouldcertainlyconsiderthehatof
ceptualissue:basically,segmentationhasaccessonlytothethegirlasanobjectdifferentfromtheblurredbackground,
descriptionsofpixels(e.g.colorlevels)andtheirspatialre-andmostwouldconsiderhershoulderasdifferentfromher
lationships,whileauseralwaysuseshigherlevelofknowl-face.Nevertheless,duetothedistributionofcolors,itisvir-
edgetoclustertheimageobjects.Withoutsuchasignifi-tuallyimpossibleforsegmentationtechniquesbasedsolely
cantpriorworldknowledge,theaccuracyofgroupingisnotonlow-levelcues,suchasthecolors,tomakeacleansepa-
meanttobeoptimalityorevennear-optimality,butratherrationoftheseregions.Therightimagedisplaystheresult
ofouralgorithm.Theregionsfoundhavewhiteborders.
∗
Thisresultispresentedmoreindepthintheexperimental
Correspondingauthor.Tel.:[phone];fax:[phone].
section(Fig.
2
).Noticefromtheresultthesegmentationof
E-mailaddresses:
[e-mail]
(R.Nock),
[e-mail]
(F.Nielsen)
thehat,cleanlyseparatedfromthebackground,andalsothe
URLs:
http://www.univ-ag.fr/
∼
rnock
,
segmentationofthegirl’schin,whichisseparatedfromher
http://www.csl.sony.co.jp/person/nielsen/
.
shoulder.
0031-3203/$30.00

2004PatternRecognitionSociety.PublishedbyElsevierLtd.Allrightsreserved.
doi:10.1016/j.patcog.2004.11.009

911232527292

Abstract
Someauthorshaverecentlydevisedadaptationsofspectralgroupingalgorithmstointegratepriorknowledge,asconstrained
eigenvaluesproblems.Inthispaper,weimproveandadaptarecentstatisticalregionmergingapproachtothistask,asanon-
parametricmixturemodelestimationproblem.Theapproachappearstobeattractivebothforitstheoreticalbenefitsandits
experimentalresults,asslightbiasbringsdramaticimprovementsoverunbiasedapproachesonchallengingdigitalpictures.

2004PatternRecognitionSociety.PublishedbyElsevierLtd.Allrightsreserved.
Keywords:
Imagesegmentation;Semi-supervisedgrouping

9113151

www.elsevier.com/locate/patcog

PatternRecognition()–

Semi-supervisedstatisticalregionrefinementforcolorimage
segmentation
ba∗,RichardNock,FrankNielsen
a
GRIMAAG
-DépartementScientifiqueInterfacultaire,UniversitédesAntilles-Guyane,CampusdeSchoelcher,BP7209,97275Schoelcher,
Martinique,France
b
SonyComputerScienceLaboratories,Inc.,3-14-13HigashiGotanda,Shinagawa-Ku,Tokyo141-0022,Japan
Received9August2004

1357

SERPNIELCITRAlabolg:NACS--aydiV:NGAPabihtarP:DE1.0.5:REVDTD5912RP)lin:.gif.loc(21-1:PPMOC:EPYT.DORP
13579113115

2

RAITPCRL2E1I9N5RPESR.Nock,F.Nielsen/PatternRecognition()–

Fig.1.Image
lena
(left),andoursegmentation(right).Inthesegmentation’sresult,regionsfoundarewhitebordered(seetextfordetails).

Fig.2.Image
lena
(upperleft),anditssegmentationby
SRRB
,withoutbias(w/o),andwithbias(w/,see
Fig.1
).Inthesegmentations’
results,regionsfoundaredelimitedwithwhiteborders.Wehave
m
=
4
,
|
V
1
|=
2
,
|
V
2
|=
2
,
|
V
3
|=
2
,
|
V
4
|=
2.Theupperrighttabledisplays
someofthelargestregionsextractedfromthesegmentation(Reg.#X).Thebottomtableshowshow
lena
’sfaceisbuiltfromthetwo
modelswhosepixels,denotedbyredtrianglesontheupperrightimage,havebeenpointedbytheuser(eachmodel’scolorisrandom);the
numberbelow
×
2000is
SRRB
’siterationnumber.

CommongroupingalgorithmsforimagesegmentationOurapproachtosegmentation,whichgivestheresultsof
useaweightedneighborhoodgraphtoformulatethespa-
Fig.1
,isbasedonasegmentationframeworkpreviously
tialrelationshipsamongpixels
[1–6]
andthenformulatethe
studiedbyYuandShi
[1,7]
:groupingwithbias.Itispar-
segmentationasagraphpartitioningproblem.Anessentialticularlyusefulfordomainsinwhichtheusermayinteract
differencebetweenthesealgorithmsisthelocalityofthewiththesegmentation,byinputtingconstraintstobiasits
groupingprocess.ShiandMalik
[3]
andYuandShi
[1,7]
result:sensormodelsinMRF
[8]
,Human–computerinter-
solveitfromaglobalstandpoint,whereasFelzenszwalbandaction,spatialattentionandothers
[1]
.Groupingwithbias
Huttenlocher
[4]
,Nock
[2]
andNielsenandNock
[5]
make
isbasicallyonestepfurthertowardstheintegrationofthe
greedylocaldecisionstomergetheconnexcomponentsofuserintheloop,comparedtothemethodofgeneralexpec-
inducedsubgraphs.Sincesegmentationisaglobaloptimiza-tationsofagoodsegmentationintegratedinnon-purposive
tionprocess,theformerapproachisaprioriagoodcan-grouping
[9]
;itissolvedbypointingintheimagesomepix-
didatetotackletheproblem,evenwhenitfacescomputa-els(the
bias
)thattheuserfeelbelongtoidentical/different
tionalcomplexityissues
[3]
.However,strongglobalproper-
objects,andthensolvingthesegmentationasaconstrained
tiescanbeobtainedforthelatterapproaches,suchasqual-groupingproblem:pixelswithidenticallabels
must
belong
itativeboundsontheoverallsegmentationerror
[4,2]
,or
tothesameregioninthesegmentation’sresult,whilepixels
evenquantitativebounds
[5]
.
withdifferentlabels
mustnot
belongtothesameregion.The

7191213252729213

135791131517191123252729213335373931434457494153555

RAITPR2195

LCENIRPESR.Nock,F.Nielsen/PatternRecognition()–

solutionfortheglobalapproachofYuandShi
[1,7]
ismath-
ematicallyappealing,butitiscomputationallydemanding,
anditrequiresquiteanextensivebiasforgoodexperimental
resultsonsmallimages.Furthermore,itmakesitdifficultto
handletheconstraintsthatsomepixelsmustnotbelongto
thesameregions;thatiswhythistechniqueismainlyused
fortheparticularbiasedsegmentationprobleminwhichone
wantstosegregatesomeobjectsfromtheirbackground.
Inthispaper,weproposeageneralsolutiontobiased
grouping,basedonalocalapproachtoimagesegmentation
[2,5]
,whichbasicallyconsistsinusingthebiasfortheesti-
mationofnon-parametricmixturemodels.Distribution-free
processingtechniquesareuseful,ifnotnecessary,ingroup-
ing
[10,2]
.However,estimatingclustersindataisalready
farfrombeingtrivialevenwhensignificativedistribution
assumptionsareassumed
[11]
.Thisiswherethebiasisof
hugeinterestwhenitcomestogrouping,asbiasdefines
partiallylabeledregions,withtheconstraintthatdifferent
labelsbelongtodifferentregions.TheapproachofNock
[2]
andNielsenandNock
[5]
isalsoconceptuallyappealing
foranadaptationtobiasedsegmentation,becauseitconsid-
ersthattheobservedimageistheresultofthesamplingof
atheoreticalimage,inwhichregionsarestatisticalregions
characterizedbydistributions.Thereisnodistributionas-
sumptiononthestatisticalpixelsofthistheoreticalimage.
Theonlyassumptionmadeisanhomogeneityproperty,ac-
cordingtowhichtheexpectationofasinglecoloristhe
sameinsideastatisticalregion,anditisdifferentbetween
twoadjacentstatisticalregions.Thesegmentationproblem
isthustheproblemofrecognizingthepartitionofthesta-
tisticalregionsonthebasisoftheobservedimage.Thebi-
asedgroupingproblemturnsouttoallowstatisticalregions
tocontaindifferentstatisticalsub-regions,notnecessarily
connected,eachsatisfyingindependentlythehomogeneity
property,
and
forwhichtheuserfeelstheyallbelongtothe
sameperceptualobject.Thus,ityieldsasignificantgeneral-
izationofthetheoreticalframeworkofNock
[2]
andNielsen
andNock
[5]
.
Ourcontributioninthispaperistwofold.First,itconsists
oftwomodificationsandimprovementstotheunbiasedseg-
mentationalgorithmofNock
[2]
andNielsenandNock
[5]
.
Theiralgorithmcontainstwostages.Informally,itsfirstpart
isaprocedurewhichordersasetofpairsofadjacentpix-
els,accordingtotheincreasingvaluesofsomereal-valued
function
f
.Itssecondpartconsistsofasinglepassonthis
order,inwhichitteststhemergingoftheregionstowhich
thepixelsbelong,usingaso-calledmergingpredicate
P
.
f
and
P
arethecornerstonesoftheapproachofNock
[2]
and
NielsenandNock
[5]
.Weproposeinthispaperabetter
f
,
andanimproved
P
relyingonaslightlymoresophisticated
statisticalanalysis.Oursecondcontributionistheextension
ofthisalgorithmtogroupingwithbias.Ourextensionkeeps
bothfastprocessingandthetheoreticalboundsonthequal-
ityofthesegmentation.Experimentally,theresultsappear
tobeveryfavorablewhencomparingthemtothoseofthe
approachofYuandShi
[1,7]
.