Deep Learning for Multimedia Processing Applications Volume Two Signal Processing and Pattern Recognition

Cover
Half Title
Title Page
Copyright Page
Contents
Chapter 1. A Review on Comparative Study of Image-Denoising in Medical Imaging
1.1 Introduction
1.1.1 Evaluation of Image-Denoising Techniques
1.1.1.1 Evaluation Metrics
1.1.2 Image-Denoising Techniques
1.1.2.1 Traditional Image-Denoising Techniques
1.1.2.1.1 Wavelet-Based Denoising
1.1.2.1.2 Median Filter
1.1.2.1.3 Total Variation
1.1.2.1.4 Deep Learning-Based
1.1.3 Applications
1.1.3.1 Image Segmentation
1.1.3.2 Image Registration
1.1.3.3 Feature Extraction
1.1.3.4 Examples
1.1.4 Comparison
1.2 Conclusion
References
Chapter 2. Remote-Sensing Image Classification: A Comprehensive Review and Applications
2.1 Introduction
2.1.1 Need for This Survey
2.1.1.1 Rapidly Evolving Field
2.1.1.2 Multidisciplinary Nature
2.1.1.3 Comprehensive Evaluation
2.1.1.4 Identification of Research Gaps
2.1.1.5 Practical Applications
2.1.2 Need for Remote-Sensing Image Classification
2.1.2.1 Rapidly Expanding Data Sources
2.1.2.2 Optimization of Deep Learning Algorithms
2.1.2.3 Transferability of Models
2.1.2.4 Standardization of Evaluation Metrics
2.1.2.5 Integration of Multi-Source Data
2.1.3 Significance of Remote-Sensing Image Classification
2.1.3.1 Improved Accuracy
2.1.3.2 Automation
2.1.3.3 Scalability
2.1.3.4 Generalizability
2.1.3.5 Interdisciplinary Applications
2.1.3.6 Innovation
2.1.3.7 Addressing Complex Problems
2.1.3.8 Improved Understanding
2.1.3.9 Career Opportunities
2.1.4 Research Gap for Deep Learning-Based Remote-Sensing Image Classification
2.1.4.1 Limited Training Data
2.1.4.2 Transferability of Models
2.1.4.3 Interpretability
2.1.4.4 Class Imbalance
2.1.4.5 Limited Understanding of Uncertainty
2.1.4.6 Limited Application to Hyperspectral Data
2.1.4.7 Limited Application to Small-Scale Features
2.2 Deep Learning Architectures for Remote-Sensing Image Classification
2.2.1 Convolutional Neural Networks (CNNs)
2.2.2 Fully Convolutional Networks (FCNs)
2.2.3 U-Net
2.2.4 SegNet
2.2.5 DeepLab
2.2.6 Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) Networks
2.2.7 Autoencoders
2.2.8 Generative Adversarial Networks (GANs)
2.2.9 Capsule Networks (CapsNets)
2.2.10 Attention-Based Mechanisms
2.2.11 Graph Convolutional Networks (GCNs)
2.2.12 Siamese Networks
2.2.13 3D Convolutional Neural Networks (3D-CNNs)
2.3 Differences in Deep Learning Architectures for Remote-Sensing Image Classification
2.3.1 Architecture
2.3.2 Input Data
2.3.3 Training Strategy
2.3.4 Transfer Learning
2.3.5 Optimization
2.3.6 Interpretability
2.4 Remote-Sensing Data Sources and Characteristics
2.4.1 Satellites
2.4.2 UAVs
2.4.3 Multispectral Sensors
2.4.4 Hyperspectral Sensors
2.4.5 Synthetic Aperture Radar (SAR)
2.4.6 Spatial Resolution
2.4.7 Spectral Resolution
2.4.8 Temporal Resolution
2.4.9 Radiometric Resolution
2.5 Application of Deep Learning in Remote Sensing
2.5.1 Land Cover Classification
2.5.2 Vegetation Monitoring
2.5.3 Urban Land-Use Classification
2.5.4 HSI Remote Sensing
2.6 Challenges for Deep Learning Methods for RS Image Processing
2.6.1 High Spatial and Spectral Variability
2.6.2 Limited Annotated Data
2.6.3 Class Imbalance
2.6.4 Intra-Class Variability
2.6.5 Spectral Mixing and Shadow Effects
2.6.6 Sensor Noise and Atmospheric Interference
2.6.7 Computational Complexity
2.6.8 Adaptability and Generalization
2.7 Limitations
2.7.1 Dependence on Image Quality
2.7.2 Temporal Variability
2.7.3 Generalization and Transferability
2.7.4 Scalability
2.7.5 Interpretability
2.7.6 Labeling Challenges
2.7.7 Privacy Concerns
2.7.8 Legal and Policy Constraints
2.8 Conclusion
2.8.1 Future Work
2.8.1.1 Environmental Monitoring and Management
2.8.1.2 Agriculture and Food Security
2.8.1.3 Disaster Response and Recovery
2.8.1.4 Urban Planning and Development
2.8.1.5 Water Resource Management
2.8.1.6 Climate Change Research
2.8.1.7 National Security and Defense
Funding
References
Chapter 3. Deep Learning Framework for Face Detection and Recognition for Dark Faces Using VGG19 with Variant of Histogram Equalization
3.1 Introduction
3.2 Literature Review
3.3 Material and Techniques
3.3.1 Convolutional Neural Network
3.3.2 Histogram Equalization
3.3.3 Data Set
3.3.4 Proposed Framework
3.4 Experiment and Results
3.4.1 Evaluation Parameter
3.4.2 Experimental Setup
3.4.3 Results and Analysis
3.5 Conclusion
References
Chapter 4. A 3D Method for Combining Geometric Verification and Volume Reconstruction in a Photo Tourism System
4.1 Introduction
4.2 Literature Review
4.3 Method
4.3.1 Processing
4.3.2 Feature Extraction
4.3.3 Feature Matching
4.3.4 Geometric Verification
4.3.5 Volumetric Reconstruction
4.3.6 Triangulation
4.3.7 Bundle Adjustment
4.4 Experiments
4.4.1 Data Set Description
4.5 Results and Analysis
4.6 Conclusion
Acknowledgments
References
Chapter 5. Deep Learning Algorithms and Architectures for Multimodal Data Analysis
5.1 Introduction to Multimodal Data Analysis
5.2 Overview of Deep Learning Algorithms and Architectures
5.2.1 Pre-Processing of the Multimodal Data
5.2.2 The Training Process of Deep Learning Models on Multimodal Data
5.2.3 Deep Learning Methods and Blockchain Technology Consortium
5.3 Conclusion and Future Directions
Abbreviations
References
Chapter 6. Deep Learning Algorithms: Clustering and Classifications for Multimedia Data
6.1 Introduction
6.1.1 Deep Learning and Its Applications in Multimedia Data Analysis
6.1.2 Classification Algorithms in Deep Learning Can Be Broadly Classified into the Following Categories
6.1.3 Deep Learning for Clustering Multimedia Data
6.1.4 Types of Clustering
6.1.5 Classification of Clustering Algorithms in Deep Learning
6.1.6 List of More Comprehensive Deep Learning Clustering Algorithms
6.2 Deep Clustering Algorithm Challenges and the Multimedia Data
6.2.1 Convolutional Autoencoders for Clustering Images
6.2.2 Deep Convolutional Embedded Clustering for Clustering Images
6.2.3 Multimodal Deep Embedded Clustering for Clustering Multimodal Data
6.2.4 Case Studies - The Effectiveness of Deep Learning for Clustering Multimodal Data
6.3 Deep Learning for Classification of Multimedia Data
6.3.1 Incorporating Multimodal Data for Improving Classification Performance
6.4 Blockchain Technology and Deep Learning Algorithms in the Context of Multimodal Data
6.4.1 Cross-Border Blockchain Technology and Deep Learning Methods
6.4.2 Key Attributes of Cross-Border Blockchain Technology
6.4.3 Cross-Border and Deep Learning Multimodal Blockchain Technology
6.4.4 Future Trends and Applications of the Blockchain and Deep Learning
6.5 Conclusion
Abbreviations
References
Chapter 7. A Non-Reference Low-Light Image Enhancement Approach Using Deep Convolutional Neural Networks
7.1 Introduction
7.2 Literature Review
7.3 Material and Techniques
7.3.1 Retinex Theory
7.3.2 Decomposition Network
7.3.3 Optimizing the Network
7.3.3.1 Spatial Consistency Loss
7.3.3.2 Exposure Control Loss
7.3.3.3 Color Consistency Loss
7.4 Experiment and Results
7.4.1 Experimental Design
7.4.2 Subjective Evaluation
7.4.3 Objective Evaluation
7.4.4 Generalization Ability
7.5 Conclusion
References
Chapter 8. Human Pose Analysis and Gesture Recognition: Methods and Applications
8.1 Introduction
8.2 Literature Review
8.2.1 Bodily Attached Sensors Based Methods
8.2.1.1 Commonly Used Methods
8.2.1.1.1 Inertial Navigation System
8.2.1.1.2 Biosensor-Based Methods
8.2.1.1.3 Pressure Transmitter-Based Methods
8.2.1.1.4 Computer Vision-Based Methods
8.2.1.1.5 Flexible Real-Time Tracking-Based Methods
8.2.1.2 Bodily Attached Smart Device-Based Methods
8.2.1.2.1 Hand Mounted-Based Methods
8.2.1.2.2 Head Mounted-Based Methods
8.2.1.2.3 Torso Wearable-Based Methods
8.2.2 Computer Vision-Based Recognition Systems
8.2.2.1 RGB Camera-Based Systems
8.2.2.2 Kinect Sensor-Based Systems
8.2.2.3 Wireless Sensor-Based Systems
8.2.3 Pose and Gesture Recognition Using Multiple Sensors
8.2.4 Data Fusion in a Multisensory Environment
8.2.4.1 Multimodality Sensor Fusion
8.2.4.1.1 Environmental and Vision Sensors
8.2.4.1.2 Vision and Wearable Sensors
8.2.4.2 Multi-Location Sensor Fusion
8.2.5 Pose and Gesture Data Set
8.3 Conclusion
References
Chapter 9. Human Action Recognition Using ConvLSTM with Adversarial Noise and Compressive-Sensing-Based Dimensionality Reduction, Concise and Informative
9.1 Introduction
9.2 Background
9.3 Proposed Model
9.3.1 Data Layer
9.3.2 Compressive Sensing
9.3.3 Feature Learning
9.3.4 Sequential Learning
9.3.5 Softmax
9.3.6 Output Layer
9.4 Results and Discussion
9.4.1 SVM Classifier
9.4.2 ConvLSTM Classifier
9.4.3 ConvLSTM with GANs
9.4.4 Overall Results
9.5 Conclusions
Supplementary Materials
Acknowledgments
References
Chapter 10. Application of Machine Learning to Urban Ecology
10.1 Introduction
10.1.1 Brief Background of Urban Ecology
10.1.2 The Importance of Machine Learning in Urban Ecology
10.1.3 Objectives of the Chapter
10.2 Introduction to Machine Learning
10.2.1 Types of Machine Learning Techniques
10.2.2 How Machine Learning Can Benefit Urban Ecology
10.3 Overview of Urban Ecological Data Sources
10.3.1 Preprocessing and Data Fusion Techniques
10.4 Applications of Machine Learning in Urban Ecosystem Services
10.4.1 Urban Green Space Identification and Monitoring
10.4.2 Biodiversity Assessment and Conservation
10.4.3 Urban Heat Island Detection and Mitigation
10.4.4 Air Quality Monitoring and Prediction
10.4.5 Flood Risk Assessment and Management
10.5 Applications of Machine Learning in Urban Landscape Planning and Design
10.5.1 Landscape Connectivity and Fragmentation Analysis
10.5.2 Green Infrastructure Planning
10.5.3 Urban Greening and Rewilding Strategies
10.5.4 Urban Form Optimization for Ecological Resilience
10.5.5 Evaluation of Landscape Design Alternatives
10.6 Machine Learning for Socio-Ecological Systems in Urban Environments
10.6.1 Analyzing Human-Nature Interactions
10.6.2 Environmental Justice and Equitable Access to Green Spaces
10.6.3 Public Engagement and Decision-Making Support
10.6.4 Community-Based Ecological Monitoring and Management
10.7 Challenges and Future Directions
10.7.1 Data Quality and Availability
10.7.2 Interpreting and Validating Machine Learning Models
10.7.3 Integrating Cross-Disciplinary Knowledge
10.7.4 Ethical Considerations
10.7.5 Climate Change and Urban Ecology
10.8 Conclusion
References
Chapter 11. Application of Machine Learning in Urban Land Use
11.1 Introduction
11.1.1 Briefly Introduce the Concept of Machine Learning and Urban Land Use
11.1.2 Explain the Significance of Integrating Machine Learning in Urban Planning and Management
11.2 Background: Understanding Urban Land Use and Machine Learning
11.2.1 Discuss the Basics of Urban Land Use, Including its Importance, Challenges, and Traditional Methods Used in Planning
11.2.2 Introduce Machine Learning and Its Key Concepts, Including Algorithms, Training, and Validation
11.2.2.1 Training Data
11.2.2.2 Validation Data
11.2.2.3 Cross-Validation
11.3 Machine Learning Techniques for Urban Land Use
11.3.1 Describe Various Machine Learning Techniques and Their Relevance to Urban Land Use Applications, Such as Classification, Regression, and Clustering Algorithms
11.3.1.1 Classification Algorithms
11.3.1.2 Regression Algorithms
11.3.1.3 Clustering Algorithms
11.3.2 Explain How Specific Algorithms, Like Convolutional Neural Networks and Support Vector Machines, can be Employed in Urban Land Use Planning
11.4 Key Applications of Machine Learning in Urban Land Use
11.4.1 Land Use Classification and Monitoring
11.4.1.1 Machine Learning
11.4.1.2 Classification
11.4.1.3 Monitoring
11.4.2 Urban Growth Modeling and Prediction
11.4.3 Transport Planning and Optimization
11.5 Data Acquisition, Processing, and Integration
11.5.1 Discuss the Importance of Quality Data for Effective Machine Learning Applications
11.5.1.1 Consequences of Poor Data Quality
11.5.1.2 Best Practices for Ensuring Data Quality
11.5.2 Explain Various Data Sources Relevant to Urban Land Use, Such as Satellite Imagery, GIS, and Open Data Platforms
11.5.3 Describe Data Pre-Processing Techniques, Including Cleaning, Normalization, and Feature Extraction
11.6 Future Trends and Opportunities
11.6.1 Explore the Future of Machine Learning in Urban Land Use, Including Advances in AI Technology, New Data Sources, and Interdisciplinary Collaboration
11.6.1.1 Advances in AI Technology
11.6.1.1.1 Deep Learning and Neural Networks
11.6.1.1.2 Generative Adversarial Networks (GANs)
11.6.1.1.3 Reinforcement Learning (RL)
11.6.1.2 New Data Sources
11.6.1.2.1 Remote Sensing and Earth Observation Data
11.6.1.2.2 Social Media and Crowdsourced Data
11.6.1.2.3 Internet of Things (IoT) and Smart Cities
11.6.1.3 Interdisciplinary Collaboration
11.6.1.3.1 Urban Planning and Geospatial Science
11.6.1.3.2 Environmental and Social Sciences
11.6.1.3.3 Public-Private Partnerships
11.7 Conclusion
11.7.1 Summarize the Key Takeaways from this Chapter
11.7.2 Reiterate the Significance of Machine Learning in Urban Land Use Planning and Management
11.7.3 Encourage Readers to Explore and Implement Machine Learning Solutions to Promote Sustainable Urban Development
11.7.3.1 Tackling Environmental Challenges with Machine Learning
11.7.3.2 Strengthening Urban Resilience Through Predictive Analytics
References
Chapter 12. Application of GIS and Remote-Sensing Technology in Ecosystem Services and Biodiversity Conservation
12.1 Introduction
12.1.1 Overview of Ecosystem Services and Biodiversity Conservation
12.1.2 Importance of GIS and Remote Sensing in Ecosystem Services and Biodiversity Conservation
12.1.3 Scope of the Chapter
12.2 Fundamentals of GIS and Remote Sensing
12.2.1 Geographic Information Systems (GISs)
12.2.1.1 Definition and Basic Concepts
12.2.1.2 Components and Data Structures
12.2.1.3 Spatial Analysis and Modeling
12.2.2 Remote Sensing
12.2.2.1 Definition and Principles
12.2.2.2 Sensors and Platforms
12.2.2.3 Image Acquisition, Processing, and Interpretation
12.3 Assessing Ecosystem Services Using GIS and Remote Sensing
12.3.1 Provisioning Services
12.3.1.1 Food and Water Resources
12.3.1.2 Raw Materials
12.3.1.3 Genetic Resources
12.3.2 Regulating Services
12.3.2.1 Climate Regulation
12.3.2.2 Water Regulation
12.3.2.3 Pest and Disease Control
12.3.3 Cultural Services
12.3.3.1 Recreation and Ecotourism
12.3.3.2 Aesthetic and Spiritual Values
12.3.3.3 Educational and Scientific Values
12.3.4 Supporting Services
12.3.4.1 Soil Formation
12.3.4.2 Nutrient Cycling
12.3.4.3 Primary Production
12.4 Biodiversity Conservation Through GIS and Remote Sensing
12.4.1 Habitat Mapping and Monitoring
12.4.1.1 Land Cover Classification
12.4.1.2 Habitat Fragmentation and Connectivity Analysis
12.4.2 Species Distribution Modeling
12.4.2.1 Predictive Modeling Techniques
12.4.2.2 Applications in Conservation Planning
12.4.3 Monitoring and Assessing Biodiversity Change
12.4.3.1 Deforestation and Reforestation
12.4.3.2 Invasive Species Detection
12.4.3.3 Climate Change Impacts on Biodiversity
12.5 Case Studies and Applications
12.5.1 Monitoring Forest Loss and Fragmentation in the Amazon Rainforest
12.5.2 Assessing the Impacts of Land-Use Change on Wetland Ecosystems
12.5.3 Predicting the Impacts of Climate Change on Mountain Biodiversity
12.5.4 Identifying Priority Areas for Coral Reef Conservation
12.5.5 Monitoring the Spread of Invasive Species in the Great Lakes Region
12.6 Challenges and Future Prospects
12.6.1 Challenges
12.6.1.1 Data Quality and Availability
12.6.1.2 Scale Mismatch
12.6.1.3 Uncertainty and Model Validation
12.6.1.4 Integration of Social and Ecological Data
12.6.2 Future Prospects
12.6.2.1 Technological Advances
12.6.2.2 Integration of Big Data and Machine Learning
12.6.2.3 Citizen Science and Crowdsourcing
12.6.2.4 Interdisciplinary Collaboration
12.7 Conclusion
References
Chapter 13. From Data Quality to Model Performance: Navigating the Landscape of Deep Learning Model Evaluation
13.1 Introduction
13.2 Importance of Data Sets, Benchmarks, and Validations
13.3 Data Sets for Deep Learning
13.3.1 What is a Data Set and Why is it Important in AI?
13.3.2 The Impact of Data Quality on Deep Learning Model Performance
13.3.3 Overview of Popular Data Sets for Deep Learning Models
13.3.4 Advantages and Limitations of Using Publicly Available Data Sets
13.3.5 Best Practices for Data Set Creation and Curation
13.3.6 Techniques for Ensuring Data Set Diversity and Balance
13.3.7 The Role of Data Augmentation in Improving Data Set Quality
13.3.8 Techniques for Labeling Data Sets Accurately and Efficiently
13.3.9 Quality of Data Set
13.4 Benchmarking for Deep Learning Model
13.4.1 Importance of Benchmarks in Evaluating the Performance of Deep Learning Models
13.4.2 Metrics Used for Benchmarking
13.4.3 Famous Benchmarks
13.4.4 Considerations for Selecting and Designing Benchmarks for Deep Learning Models
13.4.4.1 Relevance to Real-World Applications
13.4.4.2 Difficulty Level
13.4.4.3 Diversity
13.4.4.4 Reproducibility
13.4.4.5 Standardization
13.4.4.6 Scalability
13.4.4.7 Openness
13.4.4.8 Ethical Considerations
13.4.4.9 Benchmarking Tools
13.4.5 Challenges in Interpreting Benchmarks for Deep Learning Models in the Context of Specific Problems
13.5 Validations of Deep Learning Models
13.5.1 Popular Validation Techniques Used in Deep Learning Research
13.5.2 Interpreting Validation Results in the Context of Specific Problems
13.6 Challenges and Future Directions
13.6.1 Examples of Current Challenges
13.6.2 Future Directions for Improving the Development and Use of Data Sets, Benchmarks, and Validations in Deep Learning Research
13.6.2.1 Improved Data Collection and Labeling
13.6.2.2 Addressing Bias and Fairness
13.6.2.3 Addressing Overfitting and Generalization
13.6.2.4 Developing Better Metrics and Benchmarks
13.6.2.5 Automated Machine Learning
13.6.2.6 Interdisciplinary Collaboration
References
Chapter 14. Deep Learning for the Turnover Intention of Industrial Workers: Evidence from Vietnam
14.1 Introduction
14.2 Literature Review
14.2.1 Justice in an Organization and Work Interference with Personal Life and Turnover Intention
14.2.2 Research Model and Hypothesis
14.2.2.1 Competitive Working Climate (KKLV)
14.2.2.2 Procedural Salary Justice (CBCS)
14.2.2.3 Distributive Justice (CBPP)
14.2.2.4 Informational Justice (CBTD)
14.2.2.5 Interpersonal Justice (CBQH)
14.2.2.6 Work Interference with Personal Life (SXP)
14.3 Method
14.3.1 Sample
14.3.2 Scale
14.3.3 Demographics of Respondents
14.4 Results
14.4.1 Measurement Model
14.4.2 Estimation and Evaluation of the Structural Model
14.4.3 Hypothesis Testing
14.5 Conclusions
References
Chapter 15. Deep Learning for Multimedia Analysis
15.1 Introduction
15.1.1 Overview of Deep Learning
15.1.2 Applications of Deep Learning in Multimedia Analysis
15.1.3 Recent Advances in Analysis of Multimedia Using Deep Learning
15.2 Literature Review
15.3 In-Depth Learning
15.3.1 Generative Deep Architectures
15.3.1.1 Mathematical Equation for GAN
15.3.2 Discriminative Deep Architectures
15.3.2.1 Mathematical Equation for CNN
15.3.3 Hybrid Deep Architectures
15.3.3.1 Mathematical Equation for VAE
15.3.4 CNN
15.3.4.1 Mathematical Equation for CNN
15.3.5 DNN
15.3.5.1 Mathematical Equation for DNN
15.3.6 BM
15.3.6.1 Mathematical Equation for BM
15.3.7 RBM
15.3.7.1 Mathematical Equation for RBM
15.4 Multimedia Content Using Deep Learning Applications
15.4.1 Convolutional Neural Networks
15.4.2 Recurrent Speech and Natural Language Processing
15.4.3 Autoencoders are Neural Networks
15.4.4 Transfer Learning
15.4.5 Reinforcement Learning
15.4.6 Bayesian Deep Learning
15.5 Challenges and Future Directions
15.5.1 Challenges
15.5.1.1 Lack of Labeled Data
15.5.1.2 Complexity
15.5.1.3 Interpretability
15.5.1.4 Generalization
15.5.1.5 Scalability
15.5.2 Future Directions
15.5.2.1 Improving Interpretability
15.5.2.2 Incorporating Domain Knowledge
15.5.2.3 Transfer Learning
15.5.2.4 Multimodal Analysis
15.5.2.5 Developing New Architectures
15.6 Conclusions
References
Chapter 16. Challenges and Techniques to Improve Deep Detection and Recognition Methods for Text Spotting
16.1 Introduction
16.2 Challenges in Text Spotting
16.2.1 Variable Text Size and Orientation
16.2.2 Occlusion
16.2.3 Low-Quality Images
16.2.4 Large Vocabulary
16.2.5 Training Data
16.3 Deep Learning in Text Spotting
16.3.1 Convolutional Neural Networks (CNNs)
16.3.2 Recurrent Neural Networks (RNNs)
16.3.3 Convolutional Recurrent Neural Networks (CRNNs)
16.3.4 Attention Mechanisms
16.3.5 Transfer Learning
16.3.6 Lexicons
16.3.7 Language Models
16.4 Text Spotting Data Sets
16.4.1 COCO-Text
16.4.2 SynthText
16.4.3 Street View Text
16.4.4 Total-Text
16.4.5 MJSynth
16.4.6 MSRA-TD500
16.4.7 NTU-UTOI
16.4.8 FORU
16.4.9 ICDAR'19 MLT
16.4.10 Inverse-Text
16.5 DL Models used in Text Spotting
16.5.1 VGG
16.5.2 ResNet
16.5.3 Inception
16.5.4 DenseNet
16.5.5 EfficientNet
16.5.6 MobileNet
16.5.7 SSD
16.6 Loss Functions used in Text Spotting
16.6.1 Binary Cross-Entropy Loss
16.6.2 L1 or L2 Loss
16.6.3 Connectionist Temporal Classification (CTC) Loss
16.6.4 Multi-Task Loss
16.6.5 Focal Loss
16.7 Problem Definition Focused
16.8 Proposed Solution Architecture
16.8.1 ResNet Architecture
16.8.2 Loss Function Design Strategy
16.9 Data Set Preparation
16.9.1 Data Set Formulation
16.9.2 Parameter Settings
16.10 Experimental Results and Analysis
16.10.1 Environment
16.10.2 Comparison Study
16.10.3 Ablation Study
16.10.4 Key Contribution and Advantages
16.11 Conclusion and Future Work
Notes
References
Chapter 17. Leaf Classification and Disease Detection Based on R-CCN Deep Learning Approach
17.1 Introduction
17.2 Literature Review
17.3 Proposed Model and Techniques
17.3.1 Proposed Model
17.3.2 Data Pre-Processing
17.3.3 Leaf Transformation Algorithm for Training Data Set
17.3.4 RCCN Model
17.3.5 Convolutional Neural Network
17.3.6 Advancements in Technologies
17.3.7 Hardware Equipment
17.3.8 Micro USB Power Cable, Power Supply
17.3.9 Python Script
17.4 Experiment and Results
17.4.1 Experiment Process
17.4.2 Results and Analysis
17.5 Conclusion
References
Chapter 18. Multimedia Analysis with Deep Learning: Advancements & Challenges
18.1 Introduction
18.1.1 Background
18.1.2 Purpose of the Study
18.1.3 Research Questions
18.1.4 Chapter Objectives
18.1.5 Chapter Organization
18.2 Literature Review
18.2.1 Traditional Multimedia Analysis Approaches
18.2.1.1 Traditional Multimedia Analysis Approaches
18.2.1.2 Deep Learning for Multimedia Analysis
18.2.2 Deep Learning in Multimedia Analysis
18.2.3 Multimodal Learning and Cross-Modal Retrieval
18.2.3.1 Multimodal Learning
18.2.4 Challenges and Future Directions
18.3 Methodology
18.3.1 Data Set Description
18.3.2 Deep Learning Architectures
18.3.2.1 Convolutional Neural Networks (CNNs)
18.3.3 Recurrent Neural Networks (RNNs)
18.3.4 Transformers
18.3.5 Experimental Setup
18.4 Results and Discussion
18.4.1 Performance of Deep Learning Models
18.4.2 Comparison with Traditional Methods
18.4.3 Insights and Analysis
18.5 Case Studies
18.5.1 Case Study 1: Image Classification
18.5.2 Case Study 2: Video Analysis
18.5.3 Case Study 3: Multimodal Learning
18.6 Challenges and Limitations
18.6.1 Data Bias
18.6.2 Interpretability
18.6.3 Computational Efficiency
18.7 Conclusion
18.7.1 Key Findings
18.7.2 Recommendations for Future Research
References
Index