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Hybrid metaheuristics for image analysis

Author: Siddhartha Bhattacharyya
Publisher: Cham : Springer, [2018] ©2018
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Hybrid metaheuristics for image analysis.
Cham : Springer, [2018]
(OCoLC)1022789851
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Siddhartha Bhattacharyya
ISBN: 9783319776255 3319776258
OCLC Number: 1046977817
Description: 1 online resource
Contents: Intro; Preface; Contents; Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms; 1 Introduction; 1.1 Heuristic and Metaheuristic Algorithms; 1.2 Image Segmentation; 1.3 Characteristics of the Remote Sensing Images; 1.4 Satellite Image Types and Sources; 2 Evolutionary Algorithms; 2.1 Genetic Algorithm; 2.2 Hill-Climbing Algorithm; 2.3 Hybrid Genetic Algorithm; 2.4 Example of HyGA Segmentation; 3 Dynamic Genetic Algorithm; 3.1 Structure of the Dynamic Genetic Algorithm; 3.2 Example of Hybrid Dynamic GA (HyDyGA) 4 New Methods of Cooperation Between Metaheuristics and Other Algorithms4.1 Hybrid Genetic Algorithm (HyGA) and Self-Organizing Maps (SOMs); 4.2 Hybrid Dynamic (GA) and Fuzzy C-Means (FCM); 4.3 Examples of Image Segmentation Using SOMs-HyGA; 4.4 Examples of Satellite Image Segmentation Using FCM-HyDyGA; 5 Metaheuristic Performance Analysis; 5.1 Metaheuristic Algorithm Complexity Analysis; 5.2 Robustness and Efficiency Analysis; 5.3 Responsiveness Analysis; 6 Discussion; 7 Conclusion; References; A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation 1 Introduction2 Related Works; 3 Overview of Quantum Computing; 3.1 Definition of a Quantum Bit; 3.2 Quantum Register; 3.3 Quantum Measure; 3.4 Quantum Algorithms; 4 Quantum Genetic Algorithm Principles; 4.1 Coding of Quantum Chromosomes; 4.2 Measuring Chromosomes; 4.3 Quantum Genetic Operations; 5 The Proposed Approach; 5.1 From Cellular Automata to Chromosome; 5.2 Initialization; 5.3 Measure of Quantum Chromosomes; 5.4 Evaluation of Solutions; 5.5 Updating Chromosomes by Interference; 5.6 Updating of Best Solutions; 6 Experimental Results; 7 Comparison Between Quantum GA and Conventional GA 7.1 Visual Results7.2 Numerical Results; 8 Conclusion; References; Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers; 1 Introduction; 2 Feature Extraction; 2.1 Gabor Filters; 2.2 Local Binary Patterns and Orthogonal Combination of Local Binary Patterns; 2.3 Histogram of Oriented Gradients; 3 Distance-Based Classification; 4 Proposed Hybrid Metaheuristic GA-SVM Model for Classification; 4.1 Support Vector Machines; 4.2 Genetic Algorithm; 4.3 Chromosome Design; 4.4 Fitness Function; 4.5 Design of the Proposed GA-SVM Model 5 Proposed Hybrid Face Recognition Approaches5.1 Integrating OC-LBP and HOG Features; 5.2 Gabor Filtered Zernike Moments; 6 Empirical Evaluation; 6.1 Datasets Used; 6.1.1 ORL Database; 6.1.2 Yale Database; 6.1.3 FERET Database; 6.2 Implementation Parameters; 6.3 Database Generation for Validation; 6.4 Performance Evaluation of the Integrated OC-LBP and HOG Approaches; 6.5 Performance Evaluation of the Gabor Filtered ZM Method; 7 Performance Comparison with Other Similar and State-of-the-Art Methods; 8 Performance Evaluation on the Self-generated Database
Responsibility: Siddhartha Bhattacharyya, editor.

Abstract:

This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.

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