Subspace Methods for Pattern Recognition in Intelligent Environment

210 Pages·2014·15.51 MB·English

by  Yen-Wei Chen, Lakhmi C. Jain (eds.)

Studies in Computational Intelligence 552
Yen-Wei Chen
Lakhmi C. Jain
Editors
Subspace Methods for
Pattern Recognition in
Intelligent Environment
Studies in Computational Intelligence
Volume 552
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·
Yen-Wei Chen Lakhmi C. Jain
Editors
Subspace Methods for
Pattern Recognition in
Intelligent Environment
ABC
Editors
Yen-WeiChen LakhmiC.Jain
CollegeofScienceandEngineering FacultyofEducation,Science,Technology
RitsumeikanUniversity andMathematics
Kusuatsu UniversityofCanberra
Japan ACT2601
Australia
ISSN1860-949X ISSN1860-9503 (electronic)
ISBN978-3-642-54850-5 ISBN978-3-642-54851-2 (eBook)
DOI10.1007/978-3-642-54851-2
SpringerHeidelbergNewYorkDordrechtLondon
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(cid:2)c Springer-VerlagBerlinHeidelberg2014
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Foreword
Thebookisdevotedtothecontemporarymethodsforpatternrecognition,basedon
the use of the lower-dimensionalfeature subspace. The actuality of this scientific
areaisindisputableandisdefinedbytheimpetuousdevelopmentoftheinformation
theoriesand the hugeamountsofvisualdata, whichhaveto beprocessedand an-
alyzed. The large challengefor the patternrecognitionsystems is the necessity of
efficientprocessingofmultidimensionalsignalsandimages,obtainedfromvarious
sources,suchas(forexample),theEEGandECGdevices,trafficsurveillancesys-
tems,systemsforvideocontrol,admissionsystems,remotecontrolsystems,medi-
calsystemsforimagediagnostics,andsystemsforcontent-basedretrievalinlarge
image databases. The book comprises 8 chapters, which present various new ap-
proaches aimed at solving some important problems in the theory of the pattern
recognition,relatedtothefeaturespacereduction,andalsomanyinterestingappli-
cationsin the patternrecognitionarea. Each chapteris a small monograph,which
representstheresearchoftheauthorsinthecorrespondingarea.
Inthefirstchapterarepresentedandinvestigatednewapproaches,basedonthe
ActiveShapeModel(ASM)forfacerecognition.Hereisalsoproposedtheso-called
Point Distribution Model, used for the description of a set of landmarks, which
characterizetheformoftherecognizedface.Byusingthelandmarksalignmentand
the Principal ComponentAnalysis (PCA), is developedthe multi-resolutiongray-
levelfaceprofile,usedfortheimagesearch.Thepresentedmodelingresultsprove
theimprovementachieved,whencomparedtotheclassicalASM.
The second chapter is focused on one new approach for features space reduc-
tion through the conditional Statistical Shape Model (SSM), used in the medical
image analysis. In the chapter are given the advantages and the disadvantages of
this model, and are discussed its basic varieties: the non-conditional (NC-SSM),
theconventionalconditional(C-SSM),andtherelaxedconditionalStatisticalShape
Model (RC-SSM). Here is also given a comparison among various SSM models,
used for the segmentation of computer tomography images. The obtained exper-
imental results for the evaluation of the shape of the segmented areas show, that
themodelsRC-SSMandRC-SSM-E(RC-SSMwithintegratedconditionalfeatures
errormodel)surpassC-SSMandNC-SSM.
VI Foreword
Inthethirdchapterisofferedfeatureextractiontechniquebasedonthemethod
for Independent Component Analysis (ICA), aimed at the classification of High-
resolutionRemoteSensingMultispectral(MS)Images.Thetransformofthecolor
componentsR,G,Boftheinvestigated3-channelMSimagesintothecorresponding
3 independentcomponentsIC1, IC2, and IC3 results in the obtainingof nonover-
lappingdistributionsintheICAfeaturesspace.Theevaluationoftheclassification
results for the objects detected in the MS images through the K-means algorithm
showshigheraccuracyforthe ICA featuresspace, comparedto that, based onthe
PCA.
In the fourth chapter is presented the so-called Generative learning method,
throughwhich, on the basis of a small numberof real imagesof traffic signs, ob-
tainedfromTVcameras,aregeneratedmanyartificialimagesofthesesigns,which
have various degradations in respect to the originals. For the signs recognition is
used the subspace method. The presented framework is applicable for any traffic
sign by combiningit with conventionaltraffic sign detection methods. The use of
thepresentedresultsisaimedatthedriversupportsystems.
In the fifth chapterare presentedtwo novelsubspaceanalysis methodsforface
recognition and image clustering tasks. One is a nonlinear subspace method ob-
tained by using an algebraic approach, and the other is a probabilistic subspace
analysismethodderivedfromthetopicmodel.Theexperimentsonfacerecognition
andimageclusteringshowthattheproposedsubspaceanalysismethodsareresistant
inrespectoffacesvariations,suchasnoise,pose,andlighting.
The sixth chapter is devoted to the problem for the restoration of the high-
resolution image from a single low-resolution input image, by using the sparse
signal representation, based on the combinedK-SVD algorithm and the Orthogo-
nalMatchingPursuit(OMP)forlearningtheadaptivedictionaryandachievingthe
sparsecoefficients.TheOMPisanextendedorthogonalversionofmatchingpursuit,
whichisatypeofnumericaltechniquewhichinvolvesfindingthe”bestmatching”
projectionsofmultidimensionaldataontoanover-completedictionary,andcanbe
combinedintotheK-SVDstrategyforachievingsparserepresentationandthebest
adaptivedictionary.Theeffectivenessoftheproposednewdictionarypropagationin
sparsecodingforsuper-resolutionisdemonstratedbycomparisonwiththeconven-
tionalsuper-resolutionapproachessuchassparsecodingandbicubicinterpolation.
The seventhchapteris a shorttutorialonthe samplingandrecoveryof continu
ously-definedsparsesignalsanditsapplicationtoimagefeatureextraction.Tosam-
ple a signal at low frequency compared with its Nyquist frequency, the signal is
characterized using how frequently unknown parameters appear in its parametric
expression,instead ofthe classical frequencyapproach.The new frequencyofthe
parameterappearanceisdefinedastherateofinnovation.Inthechapterareanalyzed
someexamplesignalsofthiskind:asequenceofDiracsandpiecewisepolynomials,
andisalsogiventheapplicationoftheproposedalgorithmforobjectedgedetection.
In the eighth chapter is developed the approach for human face representation
throughso-called„tensorfaces“.Hereisproposedthetensor-basedsubspacelearn-
ing method (TSL) for synthesizing human multi-pose facial images from a single
two-dimensional(2D)image.Thefacialposesynthesisisappliedtogeneratemuch
Foreword VII
requiredinformationforseveralapplications,suchaspublicsecurity,facialcosme-
tology,etc.IntheproposedTSLmethod,2Dmulti-poseimagesinthedatabaseare
previously organized into a tensor form and a tensor decomposition technique is
appliedtobuildtheprojectionsubspaces.Theexperimentalresultsshowtheeffec-
tivenessofproposedmethodforfacialposesynthesis.
Eachchaptercomprisesatheoreticalpart,followedbyexperimentalresultsand
comparisonwithothersimilartechniques.Theauthorsareresearchersfromdifferent
universities and R&D centers in Japan, China, and USA. The book will be very
useful for university and PhD students, researchers and software developers, who
work in the area of the digital processing, analysis and recognitionof signals and
images.
Prof.D.Sc.PhD.RoumenKountchev
TechnicalUniversityofSofia
Bulgaria
Preface
With the fast development of internet and computer technologies, the amount of
available data is now rapidly increasing in our daily life. How to extract core in-
formation or useful features is an important issue. Subspace methods are widely
used for dimension reduction and feature extraction in pattern recognition. They
transformhighdimensionaldatatoa lowerdimensionalspace(subspace),thatfo-
cuses on the relevant information only. A lot of methods have been proposed for
datatransformation,suchasPrincipalComponentAnalysis(PCA)andIndependent
ComponentAnalysis (ICA) andso on. PCA is a linear transformthat projectsthe
dataintoanewcoordinatesystem(subspace)withbaseswherethedatavariesthe
most,whileICAfindsalinearrepresentationofnon-Gaussiandatasothatthecom-
ponents are statistically independent, or as independent as possible. Kernel PCA
andKernelICAaremodificationsoftheoriginalPCAandICA,facilitatingnonlin-
eartransformations.Alotofothernonlinearsubspacemethodsarealsoproposedin
the literature, such as ISOMAP, Locally Linear Embedding(LLE) and so on. Re-
cently,sparsecodingandsparsesamplingarehottopicsinpatternrecognitionand
signalprocessing.Formultidimensionaldatasuchasmedicalvolumes,multi-view
facialimages,tensorbasedsubspacelearningmethodsarealsoproposed,whichare
basedonamulti-linearalgebraframework.
Thisbookfocusesonmajortrendsandnewtechniquesinsubspacemethodsand
theirapplicationsinpatternrecognition.Thereareeightchapterswrittenbyexperts
inthisbook.Theareaofinterestofthechapterscoversabroadspectrumofsubspace
learningmethodswithapplicationtopatternrecognitioninintelligentenvironment.
Chapter1focusesonprincipalcomponentanalysis(PCA)anditsapplicationto
constructanactiveshapemodeloffacialimages.Thevariationsoffacialshapeand
texturecanberepresentedbyafewleadingedgemodes.Throughthismodel,image
interpretationcanbeformulatedasafittingproblem.
Chapter 2 introducesa conditionalstatistical shape model, which is a valuable
subspace methodin medicalimageanalysis. Duringtrainingof the model,the re-
lationshipbetween the shape of the objectof interestand a set of conditionalfea-
tures is established. Subsequently, while analyzing an unseen image, a measured
conditionis matched with this conditionaldistribution and then a subspace of the
X Preface
training data is marked as relevant and used for the desired reconstruction of the
objectshape.
Chapter3focusesonindependentcomponentanalysis(ICA)anditsapplication
toclassificationofhigh-resolutionremotesensingimages.ICAtriestofindalinear
representation of non-Gaussian data so that the componentsare statistically inde-
pendent, or as independentas possible. The three independentcomponentsare in
opponent-colormodelbywhichtheresponsesofR,GandBconesarecombinedin
opponentfashions.Thisisconsistentwiththeprincipleofmanycolorsystems.
Chapter4presentsatrainingmethodforsubspaceconstructionfromartificially
generatedimagesfortrafficsignrecognition,whichisoneoftheimportanttasksfor
driversupportsystems. Conventionalapproachesused camera-capturedimages as
training data, which requiredexhaustive collection of captured samples. The gen-
erativelearningmethod,instead,allowstoobtainthesetrainingimagesbasedona
smallsetofactualimages.
Chapter 5 presents local structure preservingmethods based on subspace anal-
ysis. Two novel subspace methods are proposed for face recognition and image
clusteringtasks. Thefirst isnamedSupervisedKernelLocalityPreservingProjec-
tions(SKLPP)forfacerecognitiontasks,inwhichgeometricrelationsarepreserved
accordingtopriorclass-labelinformationandcomplexnonlinearvariationsofreal
face images are represented by nonlinear kernel mapping. The second is a novel
probabilistictopicmodelforimageclusteringtask,namedDualLocalConsistency
Probabilistic Latent Semantic Analysis (DLC-PLSA). The proposed DLC-PLSA
model can learn an effective and robust mid-level representation in the latent se-
manticspaceforimageanalysis.
Chapter 6 introduces the sparse signal representation, and a popular imple-
mentation: KSVD algorithm combining orthogonal matching pursuit (OMP) for
learning the adaptive dictionary and achieving the sparse coefficients. The sparse
representationisusedforlearning-basedimagesuper-resolutionforrecoveringthe
high-resolution image from only single low-resolution one. Based on the couple
dictionary learning for super-resolution, we proposed a HR2LR (high-resolution
to low-resolution) dictionary propagation algorithm in sparse coding for image
super-resolution.
Chapter7presentsatechniqueforefficientsamplingandrecoveryofcontinuously-
definedsparsesignalsthatisknownassparsesampling.Tosampleasignalatlowfre-
quencycomparedwithitsNyquistfrequency,thesignalwascharacterizedusinghow
frequentlythe unknownparametersappearin itsparametricexpression,instead of
theclassicalfrequency.Thenewfrequencyoftheparameterappearancewasdefined
astherateofinnovation.Thechapterfocusedontwotypicalexamplesofsignalswith
finiterateofinnovation:thesequenceofDiracsandpiecewisepolynomials.
Chapter 8 presents a tensor-based subspace learning method (TSL) for synthe-
sizinghumanmulti-posefacialimagesfromasingletwo-dimensionalimage.Inthe
proposedTSLmethod,two-dimensionalmulti-poseimagesinthedatabasearepre-
viouslyorganizedintoatensorformandatensordecompositiontechniqueisapplied
to build projection subspaces. In synthesis processing, the input two-dimensional