Metaheuristic algorithms for image segmentation: theory and applications:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cham
Springer
[2019]
|
| Series: | Studies in computational intelligence
Volume 825 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xv, 226 Seiten Illustrationen, Diagramme (farbig) |
| ISBN: | 9783030129309 |
| ISSN: | 1860-949X |
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| 245 | 1 | 0 | |a Metaheuristic algorithms for image segmentation: theory and applications |c Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa |
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| 650 | 4 | |a Engineering | |
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| 700 | 1 | |a Abd Elaziz, Mohamed |4 aut | |
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Record in the Search Index
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| adam_text | Contents 1 Introduction................................................................................................ 1.1 Introduction....................................................................................... 1.2 Metaheuristic Algorithms for Optimization................................... 1.3 Image Segmentation........................................................................ 1.4 Summary................................. ................................ ..................... References............ .......................................................... 1 1 2 4 4 5 2 Optimization........................................................ 2.1 Introduction....................................................................................... 2.2 Optimization............................ 2.3 Gradient-Based Optimization....................................... 2.4 Other Classical Approaches....................... 2.5 Summary...................................................... References.................................................................................... 7 7 7 9 10 10 11 3 Metaheuristic Optimization...................................................................... 3.1 Introduction..................................... 3.1.1 Generalization of a Metaheuristic Algorithm................. 3.1.2 Problematics of Metaheuristic Algorithms..................... 3.1.3 Classification......................... 3.2 Single-Objective Optimization .. .................................................... 3.3 Multi-objective Optimization .
........................................................ 3.4 Case of Study: Particle SwarmOptimization................................ 3.4.1 Initialization........................................................................ 3.4.2 Velocity Determination.......................... 3.4.3 Particle’s Movement........................................................... 3.4.4 Selection ............................ 3.5 Summary............................................................................................ References..................................................................................................... 13 13 14 15 16 18 19 21 23 23 24 24 24 25 xi
Contents 4 Image Processing ...................................................................................... 4.1 Introduction...................................................................................... 4.2 Image Acquisition and Representation........................................... 4.2.1 Color Images................................... ................................ . 4.2.2 Pixel Properties................................................. 4.3 Histogram ........................................................................................ 4.3.1 Description of Histograms ....................... 4.3.2 Image Characteristics and Enhancement......................... 4.3.3 Color Histogram....................................................... 4.3.4 Binarization........................................................................ 4.4 Image Segmentation........................................................................ 4.4.1 Definition of Segmentation.................................... 4.4.2 Segmentation Techniques............................................ 4.5 Summaiy........................................................................................ References................................................... 27 27 28 30 32 33 33 33 37 38 39 39 40 43 44 5 Image Segmentation Using Metaheuristics.......................................... 5.1 Introduction................ 5.2 Edge-Based Image Segmentation ................... 5.3 Region-Based Image Segmentation.................................. 5.3.1 Region Growing. . .............. 5.3.2 Region
Split and Merge Method.................................. . 5.4 Data Clustering-Based Image Segmentation................................ 5.5 Comparison Between the Traditional Image Segmentation Methods................................ ..................................... ................... 5.6 Conclusions.............................................. ................................... · References........................................ ........................................................... 47 47 48 50 50 50 51 53 53 55 Multilevel Thresholdingfor Image Segmentation Based on Metaheuristic Algorithms..................................................... 6.1 Introduction.................... 6.2 Multilevel Thresholding . . ............................ 6.3 Meta-Heuristic Algorithms for Segmentation.................... 6.3.1 Cuckoo Search Algorithm.................. 6.3.2 Bat Algorithm ....................... 6.3.3 Artificial Bee Colony .. ................................................... 6.3.4 Firefly Algorithm ..................... .. . ................................... 6.3.5 Social-Spider Optimization....................................... 6.3.6 Whale Optimization Algorithm........................................ 6.3.7 Moth-Flame Optimization................ 6.3.8 Grey Wolf Optimization.................... 6.3.9 Particle Swarm Optimization. .................. 6.4 Conclusions................ References........................................ .......................................................... 59 59 60 61 61 62 62 63 63 64 64 65 65 66 66 6
Contents xjjj 7 Otsu’s Between Class Variance and the Tree Seed Algorithm .... 7.1 Introduction...................................................................... 7.2 Problem Formulation....................... ................................................. 7.3 Tree Seed Algorithm for ImageSegmentation . ........................... 7.4 Experimental and Results................................ 7.4.1 Benchmark Image .................................... 7.4.2 Parameter Settings................................................... 7.4.3 Performance Metrics................ 7.4.4 Results and Discussions ...................................... 7.5 Conclusion............................................................................. References.............................................. 71 71 72 73 74 75 75 75 77 82 82 8 Image Segmentation Using Kapur’s Entropy and a Hybrid Optimization Algorithm....................................................................... 8.1 Introduction.................. 8.2 Background........................................................ . .......................... 8.2.1 Problem Formulation . . . ................................. 8.2.2 Artificial Bee Colony................................................... 8.2.3 Salp Swarm Algorithm .. . ...................... 8.3 Proposed Approach...................................... .. ................... ............. 8.4 Experimental and Results........................................................ .. 8.4.1 Benchmark Image ............................................................. 8.4.2 Parameter
Settings............՛..., ........................................ 8.4.3 Performance Metrics....................................................v.. 8.5 Conclusion ................................................................ References.......................................................... 85 85 87 87 88 89 89 90 90 90 92 96 97 9 Tsallis Entropy for Image Thresholding................................ 9.1 Introduction............................ 9.2 Electromagnetism—Like OptimizationAlgorithm......................... 9.3 Tsallis Entropy............................................. ........................... 9.4 Multilevel Thresholding Using EMO and Tsallis Entropy......... 9.4.1 Particle Representation....................... 9.4.2 EMO Implementation......................................................... 9.4.3 Multilevel Thresholding.................................................... 9.5 Experimental Results.......................... . .......................................... 9.5.1 Result Using the TsallisEntropy........................................ 9.6 Summary...................................................... ..................................... References......................... 101 101 103 105 107 108 108 109 109 Ill 118 121 10 Image Segmentation with Minimum Cross Entropy.......................... 10.1 Introduction............ ......................... 10.2 Minimum Cross Entropy.............. ................................................ · 10.3 The Crow Search Algorithm.......................................................... 125 125 126 128
xiv Contents 10.4 Minimum Cross Entropy and Crow Search Algorithm............... 10.4.1 Solutions Representation.................................................. 10.4.2 Crow Search Implementation........................................ 10.5 Experimental Results....................................................................... 10.5.1 Parameters Setting........................................................... 10.5.2 Segmentation of Brain Images from Magnetic Resonance.......................................................................... 10.5.3 Comparative Study........................................................... 10.6 Conclusions..................................................................................... References................................................................................................... 129 130 130 131 131 131 132 136 138 11 Fuzzy Entropy Approaches for Image Segmentation...................... 11.1 Introduction..................................................................................... 11.2 Fuzzy Entropy................................................................................. 11.2.1 The Concepts of FuzzyEntropy..................................... 11.2.2 Type П Fuzzy Entropy..................................................... 11.3 Fuzzy Entropy as an OptimizationProblem................................. 11.4 Summary..........................................................................................
References................................................................................................... 141 141 142 142 143 145 145 146 12 Image Segmentation by Gaussian Mixture................ 12.1 Introduction.................. ....................................................... .......... 12.2 Theory of Gaussian Approximation......... .. . . . ......................... 12.3 Extraction of the Threshold Values ,. ................................... .. · · 12.3.1 Solutions Representation.......................... 12.3.2 An Example of Image Segmentation . ..................... .. . · 12.4 Summary................ References....................................... ............................. ...................... 149 149 149 151 152 153 154 155 13 Image Segmentation as a Multiobjective Optimization Problem ... 13.1 Introduction ........................................................................ 13.2 Problem Definition.............................. .............. ........... ............... 13.2.1 Kapur’s Entropy......................................... ............... .. 13.2.2 Otsu’s Function.............................................. ............. · · 13.2.3 Multiobjective Optimization ............................ 13.3 Multiobjective Grey Wolf Optimizer ............................................ 13.3.1 Standard Grey Wolf Optimizer . . . ................................. 13.3.2 Multi Objective GWO forImage Segmentation............ 13.4 Experimental Results. .................. 13.4.1 Dataset Description ....................................... 13.4.2
Performance Measures .. ...................... 13.4.3 Result and Discussion....................................................... 13.4.4 Statistical Analysis................................ 157 157 159 160 160 161 161 162 163 165 165 168 169 171
Contents XV 13.5 Conclusions....................................................................................... References..................................................................................................... 177 177 14 Clustering Algorithmsfor Image Segmentation.................................. 14.1 Introduction....................................................................................... 14.2 Clustering Methods for Image Segmentation................................. 14.2.1 Hierarchical Clustering...................................................... 14.2.2 Partitional Clustering......................................................... 14.2.3 К-Means Clustering........................................................... 14.2.4 Fuzzy Clustering . . . ......................................................... 14.3 Summary............................................................................................ References..................................................................................................... 181 181 182 183 185 185 187 188 189 15 ContextualInformation in Image Thresholding................................... 15.1 Introduction....................................................................................... 15.2 Energy Curve..................................................................................... 15.3 Image Segmentation Using ALO.................................................... 15.3.1 Antiion Optimizer for Image Thresholding..................... 15.4 Experimental
Results......................................................................... 15.4.1 Results Using Otsu’s Method.......................................... 15.4.2 Results Using MaximumEntropy..................................... 15.4.3 Statistical Comparison...................................................... 15.5 Summary............................................................................................ References..................................................................................................... 191 191 192 194 195 196 199 200 222 224 225
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| author | Oliva, Diego Abd Elaziz, Mohamed Hinojosa, Salvador |
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| indexdate | 2024-07-10T08:21:33Z |
| institution | BVB |
| isbn | 9783030129309 |
| issn | 1860-949X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030944956 |
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| physical | xv, 226 Seiten Illustrationen, Diagramme (farbig) |
| publishDate | 2019 |
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| publisher | Springer |
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| series | Studies in computational intelligence |
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| spelling | Oliva, Diego (DE-588)1126046639 aut Metaheuristic algorithms for image segmentation: theory and applications Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa Cham Springer [2019] © 2019 xv, 226 Seiten Illustrationen, Diagramme (farbig) txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence Volume 825 1860-949X Computational Intelligence Artificial Intelligence Signal, Image and Speech Processing Engineering Artificial intelligence Abd Elaziz, Mohamed aut Hinojosa, Salvador aut Erscheint auch als Online-Ausgabe 978-3-030-12931-6 Studies in computational intelligence Volume 825 (DE-604)BV020822171 825 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030944956&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Oliva, Diego Abd Elaziz, Mohamed Hinojosa, Salvador Metaheuristic algorithms for image segmentation: theory and applications Studies in computational intelligence Computational Intelligence Artificial Intelligence Signal, Image and Speech Processing Engineering Artificial intelligence |
| title | Metaheuristic algorithms for image segmentation: theory and applications |
| title_auth | Metaheuristic algorithms for image segmentation: theory and applications |
| title_exact_search | Metaheuristic algorithms for image segmentation: theory and applications |
| title_full | Metaheuristic algorithms for image segmentation: theory and applications Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa |
| title_fullStr | Metaheuristic algorithms for image segmentation: theory and applications Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa |
| title_full_unstemmed | Metaheuristic algorithms for image segmentation: theory and applications Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa |
| title_short | Metaheuristic algorithms for image segmentation: theory and applications |
| title_sort | metaheuristic algorithms for image segmentation theory and applications |
| topic | Computational Intelligence Artificial Intelligence Signal, Image and Speech Processing Engineering Artificial intelligence |
| topic_facet | Computational Intelligence Artificial Intelligence Signal, Image and Speech Processing Engineering Artificial intelligence |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030944956&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
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