Quantum Inspired Meta-heuristics for Image Analysis

368 Pages·2019·43.786 MB·English

by  Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik

QuantumInspiredMeta-heuristicsforImageAnalysis
Quantum Inspired Meta-heuristics for
Image Analysis
SandipDey
GlobalInstituteofManagementandTechnology
Krishnanagar,Nadia,WestBengal
India
SiddharthaBhattacharyya
RCCInstituteofInformationTechnology
Kolkata
India
UjjwalMaulik
JadavpurUniversity
Kolkata
India
Thiseditionfirstpublished2019
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LibraryofCongressCataloging-in-PublicationData
Names:Dey,Sandip,1977-author.|Bhattacharyya,Siddhartha,1975-author.
|Maulik,Ujjwal,author.
Title:QuantumInspiredMeta-heuristicsforImageAnalysis/Dr.SandipDey
(GlobalInstituteofManagementandTechnologyKrishnanagar,Nadia,WestBengal,India),
ProfessorSiddharthaBhattacharyya(RCCInstituteofInformationTechnology,Kolkata,India),
ProfessorUjjwalMaulik(JadavpurUniversity,Kolkata,India).
Description:Hoboken,NJ:Wiley,2019.|Includesbibliographicalreferences
andindex.|
Identifiers:LCCN2019001402(print)|LCCN2019004054(ebook)|ISBN
9781119488774(AdobePDF)|ISBN9781119488781(ePub)|ISBN9781119488750
(hardcover)
Subjects:LCSH:Imagesegmentation.|Imageanalysis.|Metaheuristics.|
Heuristicalgorithms.
Classification:LCCTA1638.4(ebook)|LCCTA1638.4.D492019(print)|DDC
006.4/2015181–dc23
LCrecordavailableathttps://lccn.loc.gov/2019001402
CoverDesign:Wiley
CoverImage:©issaroprakalung/Shutterstock
Setin10/12ptWarnockProbySPiGlobal,Chennai,India
10 9 8 7 6 5 4 3 2 1
SandipDeywouldliketodedicatethisbooktothelovingmemoryofhisfather,thelate
DhananjoyDey,hismotherSmt.GitaDey,hiswifeSwagataDeySarkar,hischildren
SunishkaandShriaan,hissiblingsKakali,Tanusree,SanjoyandhisnephewsShreyash
andAdrishan.
SiddharthaBhattacharyyawouldliketodedicatethisbooktohisfather,thelateAjit
KumarBhattacharyya,hismother,thelateHashiBhattacharyya,hisbelovedwife
RashniBhattacharyya,andPadmashreeProfessorAjoyKumarRay,Honorable
Chairman,RCCInstituteofInformationTechnology,Kolkata,India.
UjjwalMaulikwouldliketodedicatethisbooktohissonUtsav,hiswifeSanghamitra
andallhisstudentsandfriends.
vii
Contents
Preface xiii
Acronyms xv
1 Introduction 1
1.1 ImageAnalysis 3
1.1.1 ImageSegmentation 4
1.1.2 ImageThresholding 5
1.2 PrerequisitesofQuantumComputing 7
1.2.1 Dirac’sNotation 8
1.2.2 Qubit 8
1.2.3 QuantumSuperposition 8
1.2.4 QuantumGates 9
1.2.4.1 QuantumNOTGate(MatrixRepresentation) 9
1.2.4.2 QuantumZGate(MatrixRepresentation) 9
1.2.4.3 HadamardGate 10
1.2.4.4 PhaseShiftGate 10
1.2.4.5 ControlledNOTGate(CNOT) 10
1.2.4.6 SWAPGate 11
1.2.4.7 ToffoliGate 11
1.2.4.8 FredkinGate 12
1.2.4.9 QuantumRotationGate 13
1.2.5 QuantumRegister 14
1.2.6 QuantumEntanglement 14
1.2.7 QuantumSolutionsofNP-completeProblems 15
1.3 RoleofOptimization 16
1.3.1 Single-objectiveOptimization 16
1.3.2 Multi-objectiveOptimization 18
1.3.3 ApplicationofOptimizationtoImageAnalysis 18
1.4 RelatedLiteratureSurvey 19
1.4.1 Quantum-basedApproaches 19
1.4.2 Meta-heuristic-basedApproaches 21
1.4.3 Multi-objective-basedApproaches 22
viii Contents
1.5 OrganizationoftheBook 23
1.5.1 QuantumInspiredMeta-heuristicsforBi-levelImageThresholding 24
1.5.2 QuantumInspiredMeta-heuristicsforGray-scaleMulti-levelImage
Thresholding 24
1.5.3 QuantumBehavedMeta-heuristicsforTrueColorMulti-level
Thresholding 24
1.5.4 QuantumInspiredMulti-objectiveAlgorithmsforMulti-levelImage
Thresholding 24
1.6 Conclusion 25
1.7 Summary 25
ExerciseQuestions 26
2 ReviewofImageAnalysis 29
2.1 Introduction 29
2.2 Definition 29
2.3 MathematicalFormalism 30
2.4 CurrentTechnologies 30
2.4.1 DigitalImageAnalysisMethodologies 31
2.4.1.1 ImageSegmentation 31
2.4.1.2 FeatureExtraction/Selection 32
2.4.1.3 Classification 34
2.5 OverviewofDifferentThresholdingTechniques 35
2.5.1 Ramesh’sAlgorithm 35
2.5.2 Shanbag’sAlgorithm 36
2.5.3 CorrelationCoefficient 37
2.5.4 Pun’sAlgorithm 38
2.5.5 Wu’sAlgorithm 38
2.5.6 Renyi’sAlgorithm 39
2.5.7 Yen’sAlgorithm 39
2.5.8 Johannsen’sAlgorithm 40
2.5.9 Silva’sAlgorithm 40
2.5.10 FuzzyAlgorithm 41
2.5.11 Brink’sAlgorithm 41
2.5.12 Otsu’sAlgorithm 43
2.5.13 Kittler’sAlgorithm 43
2.5.14 Li’sAlgorithm 44
2.5.15 Kapur’sAlgorithm 44
2.5.16 Huang’sAlgorithm 45
2.6 ApplicationsofImageAnalysis 46
2.7 Conclusion 47
2.8 Summary 48
ExerciseQuestions 48
3 OverviewofMeta-heuristics 51
3.1 Introduction 51
3.1.1 ImpactonControllingParameters 52
3.2 GeneticAlgorithms 52
Contents ix
3.2.1 FundamentalPrinciplesandFeatures 53
3.2.2 Pseudo-codeofGeneticAlgorithms 53
3.2.3 EncodingStrategyandtheCreationofPopulation 54
3.2.4 EvaluationTechniques 54
3.2.5 GeneticOperators 54
3.2.6 SelectionMechanism 54
3.2.7 Crossover 55
3.2.8 Mutation 56
3.3 ParticleSwarmOptimization 56
3.3.1 Pseudo-codeofParticleSwarmOptimization 57
3.3.2 PSO:VelocityandPositionUpdate 57
3.4 AntColonyOptimization 58
3.4.1 StigmergyinAnts:BiologicalInspiration 58
3.4.2 Pseudo-codeofAntColonyOptimization 59
3.4.3 PheromoneTrails 59
3.4.4 UpdatingPheromoneTrails 59
3.5 DifferentialEvolution 60
3.5.1 Pseudo-codeofDifferentialEvolution 60
3.5.2 BasicPrinciplesofDE 61
3.5.3 Mutation 61
3.5.4 Crossover 61
3.5.5 Selection 62
3.6 SimulatedAnnealing 62
3.6.1 Pseudo-codeofSimulatedAnnealing 62
3.6.2 BasicsofSimulatedAnnealing 63
3.7 TabuSearch 64
3.7.1 Pseudo-codeofTabuSearch 64
3.7.2 MemoryManagementinTabuSearch 65
3.7.3 ParametersUsedinTabuSearch 65
3.8 Conclusion 65
3.9 Summary 65
ExerciseQuestions 66
4 QuantumInspiredMeta-heuristicsforBi-levelImage
Thresholding 69
4.1 Introduction 69
4.2 QuantumInspiredGeneticAlgorithm 70
4.2.1 InitializethePopulationofQubitEncodedChromosomes 71
4.2.2 PerformQuantumInterference 72
4.2.2.1 GenerateRandomChaoticMapforEachQubitState 72
4.2.2.2 InitiateProbabilisticSwitchingBetweenChaoticMaps 73
4.2.3 FindtheThresholdValueinPopulationandEvaluateFitness 74
4.2.4 ApplySelectionMechanismtoGenerateaNewPopulation 74
4.2.5 FoundationofQuantumCrossover 74
4.2.6 FoundationofQuantumMutation 74
4.2.7 FoundationofQuantumShift 75
4.2.8 ComplexityAnalysis 75
x Contents
4.3 QuantumInspiredParticleSwarmOptimization 76
4.3.1 ComplexityAnalysis 77
4.4 ImplementationResults 77
4.4.1 ExperimentalResults(PhaseI) 79
4.4.1.1 ImplementationResultsforQEA 91
4.4.2 ExperimentalResults(PhaseII) 96
4.4.2.1 ExperimentalResultsofProposedQIGAandConventionalGA 96
4.4.2.2 ResultsObtainedwithQEA 96
4.4.3 ExperimentalResults(PhaseIII) 114
4.4.3.1 ResultsObtainedwithProposedQIGAandConventionalGA 114
4.4.3.2 ResultsobtainedfromQEA 117
4.5 ComparativeAnalysisamongtheParticipatingAlgorithms 120
4.6 Conclusion 120
4.7 Summary 121
ExerciseQuestions 121
CodingExamples 123
5 QuantumInspiredMeta-HeuristicsforGray-ScaleMulti-LevelImage
Thresholding 125
5.1 Introduction 125
5.2 QuantumInspiredGeneticAlgorithm 126
5.2.1 PopulationGeneration 126
5.2.2 QuantumOrthogonality 127
5.2.3 DeterminationofThresholdValuesinPopulationandMeasurement
ofFitness 128
5.2.4 Selection 129
5.2.5 QuantumCrossover 129
5.2.6 QuantumMutation 129
5.2.7 ComplexityAnalysis 129
5.3 QuantumInspiredParticleSwarmOptimization 130
5.3.1 ComplexityAnalysis 131
5.4 QuantumInspiredDifferentialEvolution 131
5.4.1 ComplexityAnalysis 132
5.5 QuantumInspiredAntColonyOptimization 133
5.5.1 ComplexityAnalysis 133
5.6 QuantumInspiredSimulatedAnnealing 134
5.6.1 ComplexityAnalysis 136
5.7 QuantumInspiredTabuSearch 136
5.7.1 ComplexityAnalysis 136
5.8 ImplementationResults 137
5.8.1 ConsensusResultsoftheQuantumAlgorithms 142
5.9 ComparisonofQIPSOwithOtherExistingAlgorithms 145
5.10 Conclusion 165
5.11 Summary 166
ExerciseQuestions 167
CodingExamples 190
Contents xi
6 QuantumBehavedMeta-HeuristicsforTrueColorMulti-LevelImage
Thresholding 195
6.1 Introduction 195
6.2 Background 196
6.3 QuantumInspiredAntColonyOptimization 196
6.3.1 ComplexityAnalysis 197
6.4 QuantumInspiredDifferentialEvolution 197
6.4.1 ComplexityAnalysis 200
6.5 QuantumInspiredParticleSwarmOptimization 200
6.5.1 ComplexityAnalysis 200
6.6 QuantumInspiredGeneticAlgorithm 201
6.6.1 ComplexityAnalysis 203
6.7 QuantumInspiredSimulatedAnnealing 203
6.7.1 ComplexityAnalysis 204
6.8 QuantumInspiredTabuSearch 204
6.8.1 ComplexityAnalysis 206
6.9 ImplementationResults 207
6.9.1 ExperimentalResults(PhaseI) 209
6.9.1.1 TheStabilityoftheComparableAlgorithms 210
6.9.2 ThePerformanceEvaluationoftheComparableAlgorithmsofPhaseI 225
6.9.3 ExperimentalResults(PhaseII) 235
6.9.4 ThePerformanceEvaluationoftheParticipatingAlgorithmsofPhaseII 235
6.10 Conclusion 294
6.11 Summary 294
ExerciseQuestions 295
CodingExamples 296
7 QuantumInspiredMulti-objectiveAlgorithmsforMulti-levelImage
Thresholding 301
7.1 Introduction 301
7.2 Multi-objectiveOptimization 302
7.3 ExperimentalMethodologyforGray-ScaleMulti-LevelImage
Thresholding 303
7.3.1 QuantumInspiredNon-dominatedSorting-BasedMulti-objectiveGenetic
Algorithm 303
7.3.2 ComplexityAnalysis 305
7.3.3 QuantumInspiredSimulatedAnnealingforMulti-objectiveAlgorithms 305
7.3.3.1 ComplexityAnalysis 307
7.3.4 QuantumInspiredMulti-objectiveParticleSwarmOptimization 308
7.3.4.1 ComplexityAnalysis 309
7.3.5 QuantumInspiredMulti-objectiveAntColonyOptimization 309
7.3.5.1 ComplexityAnalysis 310
7.4 ImplementationResults 311
7.4.1 ExperimentalResults 311
7.4.1.1 TheResultsofMulti-LevelThresholdingforQINSGA-II,NSGA-II,and
SMS-EMOA 312
7.4.1.2 TheStabilityoftheComparableMethods 312