Deep learning for image classification in different color systems
General Information:
Level |
Master |
Title |
Deep learning for image classification in different color systems |
Specialty |
génie logiciel |
Cover Page:
Outline:
INTRODUCTION
1.1 Background
1.2 The Scope of this Research
1.3 Research Goals and Questions
1.4 Structure
Contents
CHAPTER I: DEEP LEARNING AND ITS APPLICATION
I.1 Historical Context
I.1.1 Artificial intelligence
I.1.2 Machine learning
I.1.2.1 Supervised learning
I.1.2.2 Unsupervised learning
I.1.2.3 Semi-supervised
I.1.2.4 Shallow Learning
1.2 Deep Learning
1.2.1 The Neuron
1.2.2 Artificial neuron
1.2.3 Artificial Neural Networks
1.2.3.1 Input layer
1.2.3.2 Hidden layer
1.2.3.3 Output layer
1.2.3.4 Activation Functions
1.2.3.4.1 Linear
1.2.3.4.2 Non-linearity activation function
1.2.3.5 Loss Functions
1.2.4 Gradient Descent
1.2.4.1 Gradient Descent with Sigmoidal Neurons
1.2.5 The Back-propagation Algorithm
I.2.6 convolutional Neural Networks (CNNs / ConvNets)
I.2.6.1 Convolution Operation
I.2.6.2Architecture of CNN
I.2.6.2.1 Convolution Layer
I.2.6.2.1.1 Filters/Kernels
I.2.6.2.1.2Hyperparameters
I.2.6.2.2 Pooling Layer
I.3 Regularization for Deep Learning
I.3.1 Data-set Augmentation
I.3.2 Dropout
I.4 Deep Learning Convolutional neural networks Architectures
I.4.1 ResNet
I.4.2 Capsule Network
CHAPTER II: COLOR SYSTEMS
II.1 RGB Color Images
II.2 HSV Color Space
II.2.1 RGB-HSV
II.3 HLS Color Space
II.3.1 RGB→HLS
II.4 TV Color Spaces YUV
II.5 Colorimetric Color Spaces
II.5.1 CIE Color Spaces
II.5.1.1 CIE XYZ color space
II.6 sRGB
II.6.1 Transformation CIE XYZ→SRGB
II.6.2 Transformation sRGB→CIE XYZ
II.6.3 Calculating with sRGB values
II.7 CIE LUV (CIE L *u* v*)color space
II.8 The 1976 CIE L*a*b*)color space
CHPTER III EXPERIMENTATION AND RESULTS
III.1 Converting CIFAR10 dataset to different colorspace
III.2 Libraries
III.2.1 Keras
III.2.2 Tensorflow
III.2.3 scikit-image
III.3 Implementation
III.4 EXPERIMENTS
III.5 Discussion RESULTS
CONCLUSION
Bibliography
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