Fundamentals of Image Data Mining: Analysis, Features, Classification and Retrieval (Texts in Computer Science)

Preface
About This Book
	Key Features
	New Features of the Second Edition
Contents
About the Author
List of Figures
List of Tables
Preliminaries
1 Fourier Transform
	1.1 Introduction
	1.2 Fourier Series
		1.2.1 Sinusoids
		1.2.2 Fourier Series
		1.2.3 Complex Fourier Series
	1.3 Fourier Transform
	1.4 Discrete Fourier Transform
		1.4.1 DFT
		1.4.2 Uncertainty Principle
		1.4.3 Nyquist Theorem
	1.5 2D Fourier Transform
	1.6 Properties of 2D Fourier Transform
		1.6.1 Separability
		1.6.2 Translation
		1.6.3 Rotation
		1.6.4 Scaling
		1.6.5 Convolution Theorem
	1.7 Techniques of Computing FT Spectrum
	1.8 Summary
	1.9 Exercises
	References
2 Windowed Fourier Transform
	2.1 Introduction
	2.2 Short-Time Fourier Transform
		2.2.1 Spectrogram
	2.3 Gabor Filters
		2.3.1 Gabor Transform
		2.3.2 Design of Gabor Filters
		2.3.3 Spectra of Gabor Filters
	2.4 Discrete Cosine Transform
		2.4.1 1D DCT Model
		2.4.2 DCT Bases
		2.4.3 2D DCT
		2.4.4 Computation of 2D DCT
	2.5 Summary
	2.6 Exercises
	References
3 Wavelet Transform
	3.1 Discrete Wavelet Transform
	3.2 Multiresolution Analysis
	3.3 Fast Wavelet Transform
		3.3.1 DTW Decomposition Tree
		3.3.2 1D Haar Wavelet Transform
		3.3.3 2D Haar Wavelet Transform
		3.3.4 Application of DWT on Image
	3.4 Summary
	3.5 Exercises
Image Representation and Feature Extraction
4 Color Feature Extraction
	4.1 Introduction
	4.2 Color Space
		4.2.1 CIE XYZ, xyY Color Spaces
		4.2.2 RGB Color Space
		4.2.3 HSV, HSL and HSI Color Spaces
		4.2.4 CIE LUV Color Space
		4.2.5 Y′CbCr Color Space
	4.3 Image Clustering and Segmentation
		4.3.1 K-means Clustering
		4.3.2 JSEG Segmentation
	4.4 Color Feature Extraction
		4.4.1 Color Histogram
			4.4.1.1 Component Histogram
			4.4.1.2 Indexed Color Histogram
			4.4.1.3 Dominant Color Histogram
		4.4.2 Color Structure Descriptor
		4.4.3 Dominant Color Descriptor
		4.4.4 Color Coherence Vector
		4.4.5 Color Correlogram
		4.4.6 Color Layout Descriptor
		4.4.7 Scalable Color Descriptor
		4.4.8 Color Moments
	4.5 Image Enhancement
		4.5.1 Noise Removal
		4.5.2 Contrast Enhancement
	4.6 Summary
	4.7 Exercises
	References
5 Texture Feature Extraction
	5.1 Introduction
	5.2 Spatial Texture Feature Extraction Methods
		5.2.1 Tamura Textures
		5.2.2 Gray-Level Co-Occurrence Matrices
		5.2.3 Markov Random Field
		5.2.4 Fractal Dimension
		5.2.5 Discussions
	5.3 Spectral Texture Feature Extraction Methods
		5.3.1 DCT-Based Texture Feature
		5.3.2 Texture Features Based on Gabor Filters
			5.3.2.1 Gabor Filters
			5.3.2.2 Gabor Spectrum
			5.3.2.3 Texture Representation
			5.3.2.4 Rotation Invariant Gabor Features
		5.3.3 Texture Features Based on Wavelet Transform
			5.3.3.1 Selection and Application of Wavelets
			5.3.3.2 Contrast of DWT and Other Spectral Transforms
			5.3.3.3 Multiresolution Analysis
		5.3.4 Texture Features Based on Curvelet Transform
			5.3.4.1 Curvelet Transform
			5.3.4.2 Discrete Curvelet Transform
			5.3.4.3 Curvelet Spectra
			5.3.4.4 Curvelet Features
		5.3.5 Discussions
	5.4 Summary
	5.5 Exercises
	References
6 Shape Representation
	6.1 Introduction
	6.2 Perceptual Shape Descriptors
		6.2.1 Circularity and Compactness
		6.2.2 Eccentricity and Elongation
		6.2.3 Convexity and Solidarity
		6.2.4 Euler Number
		6.2.5 Bending Energy
	6.3 Contour-Based Shape Methods
		6.3.1 Shape Signatures
			6.3.1.1 Position Function
			6.3.1.2 Centroid Distance
			6.3.1.3 Angular Functions
			6.3.1.4 Curvature Signature
			6.3.1.5 Area Function
			6.3.1.6 Discussions
		6.3.2 Shape Context
		6.3.3 Boundary Moments
		6.3.4 Stochastic Method
		6.3.5 Scale Space Method
			6.3.5.1 Scale Space
			6.3.5.2 Curvature Scale Space
		6.3.6 Fourier Descriptor
		6.3.7 Discussions
		6.3.8 Syntactic Analysis
		6.3.9 Polygon Decomposition
			6.3.9.1 Chain Code Representation
			6.3.9.2 Smooth Curve Decomposition
			6.3.9.3 Discussions
	6.4 Region-Based Shape Feature Extraction
		6.4.1 Geometric Moments
		6.4.2 Complex Moments
		6.4.3 Generic Fourier Descriptor
		6.4.4 Shape Matrix
		6.4.5 Shape Profiles
			6.4.5.1 Shape Projections
			6.4.5.2 Radon Transform
		6.4.6 Discussions
		6.4.7 Convex Hull
		6.4.8 Medial Axis
	6.5 Summary
	6.6 Exercises
	References
Image Classification and Annotation
7 Bayesian Classification
	7.1 Introduction
	7.2 Naïve Bayesian Image Classification
		7.2.1 NB Formulation
		7.2.2 NB with Independent Features
		7.2.3 NB with Bag of Features
	7.3 Image Annotation with Word Co-occurrence
	7.4 Image Annotation with Joint Probability
	7.5 Cross-Media Relevance Model
	7.6 Image Annotation with Parametric Model
	7.7 Image Classification with Gaussian Process
	7.8 Summary
	7.9 Exercises
	References
8 Support Vector Machine
	8.1 Linear Classifier
		8.1.1 A Theoretical Solution
		8.1.2 An Optimal Solution
		8.1.3 A Suboptimal Solution
	8.2 K Nearest Neighbor Classification
	8.3 Support Vector Machine
		8.3.1 The Perceptron
		8.3.2 SVM—The Primal Form
			8.3.2.1 The Margin Between Two Classes
			8.3.2.2 Margin Maximization
			8.3.2.3 The Primal Form of SVM
		8.3.3 The Dual Form of SVM
			8.3.3.1 The Dual-Form Perceptron
		8.3.4 Kernel-Based SVM
			8.3.4.1 The Dual-Form SVM Versus NN Classifier
			8.3.4.2 Kernel Definition
			8.3.4.3 Building New Kernels
			8.3.4.4 The Kernel Trick
		8.3.5 The Pyramid Match Kernel
		8.3.6 Discussions
	8.4 Fusion of SVMs
		8.4.1 Fusion of Binary SVMs
		8.4.2 Multilevel Fusion of SVMs
		8.4.3 Fusion of SVMs with Different Features
	8.5 Summary
	8.6 Exercises
	References
9 Artificial Neural Network
	9.1 Introduction
	9.2 Artificial Neurons
		9.2.1 An AND Neuron
		9.2.2 An OR Neuron
	9.3 Perceptron
	9.4 Nonlinear Neural Network
	9.5 Activation and Inhibition
		9.5.1 Sigmoid Activation
		9.5.2 Shunting Inhibition
	9.6 The Backpropagation Neural Network
		9.6.1 The BP Network and Error Function
		9.6.2 Layer K Weight Estimation and Updating
		9.6.3 Layer K−1 Weight Estimation and Updating
		9.6.4 The BP Algorithm
	9.7 Convolutional Neural Network
		9.7.1 CNN Architecture
		9.7.2 Input Layer
		9.7.3 Convolution Layer 1 (C1)
			9.7.3.1 2D Convolution
			9.7.3.2 Stride and Padding
			9.7.3.3 Bias
			9.7.3.4 Volume Convolution in Layer C1
			9.7.3.5 Depth of the Feature Map Volume
			9.7.3.6 ReLU Activation
			9.7.3.7 Batch Normalization
		9.7.4 Pooling or Subsampling Layer 1 (S1)
		9.7.5 Convolution Layer 2 (C2)
		9.7.6 Hyperparameters
	9.8 Implementation of CNN
		9.8.1 CNN Architecture
		9.8.2 Filters of the Convolution Layers
		9.8.3 Filters of the Fully Connected Layers
		9.8.4 Feature Maps of Convolution Layers
		9.8.5 Matlab Implementation
	9.9 Summary
	9.10 Exercises
	References
10 Image Annotation with Decision Tree
	10.1 Introduction
	10.2 ID3
		10.2.1 ID3 Splitting Criterion
	10.3 C4.5
		10.3.1 C4.5 Splitting Criterion
	10.4 CART
		10.4.1 Classification Tree Splitting Criterion
		10.4.2 Regression Tree Splitting Criterion
		10.4.3 Application of Regression Tree
	10.5 DT for Image Classification
		10.5.1 Feature Discretization
		10.5.2 Building the DT
		10.5.3 Image Classification and Annotation with DT
	10.6 Summary
	10.7 Exercises
	References
Image Retrieval and Presentation
11 Image Indexing
	11.1 Numerical Indexing
		11.1.1 List Indexing
		11.1.2 Tree Indexing
	11.2 Inverted File Indexing
		11.2.1 Inverted File for Textual Documents Indexing
		11.2.2 Inverted File for Image Indexing
			11.2.2.1 Determine the Area Weight aw
			11.2.2.2 Determine the Position Weight pw
			11.2.2.3 Determine the Relationship Weight rw
			11.2.2.4 Inverted File for Image Indexing
	11.3 Summary
	11.4 Exercises
	References
12 Image Ranking
	12.1 Introduction
	12.2 Similarity Measures
		12.2.1 Distance Metric
		12.2.2 Minkowski-Form Distance
		12.2.3 Mass-Based Distance
		12.2.4 Cosine Distance
		12.2.5 χ2 Statistic
		12.2.6 Histogram Intersection
		12.2.7 Quadratic Distance
		12.2.8 Mahalanobis Distance
	12.3 Performance Measures
		12.3.1 Recall and Precision Pair (RPP)
		12.3.2 F-measure
		12.3.3 Percentage of Weighted Hits (PWH)
		12.3.4 Percentage of Similarity Ranking (PSR)
		12.3.5 Bullseye Accuracy
	12.4 Hypothesis Testing
		12.4.1 Introduction
		12.4.2 Fundamental Theorems of Statistics
		12.4.3 Properties of Normal Distribution
		12.4.4 HT on a Single Population
		12.4.5 Power of Test
		12.4.6 HT on Difference of Means
		12.4.7 Summary of HT
		12.4.8 Margin of Error
	12.5 Summary
	12.6 Exercises
	References
13 Image Presentation
	13.1 Introduction
	13.2 Caption Browsing
	13.3 Category Browsing
		13.3.1 Category Browsing on the Web
		13.3.2 Hierarchical Category Browsing
	13.4 Content Browsing
		13.4.1 Content Browsing in 3D Space
		13.4.2 Content Browsing with Fish Eye View
		13.4.3 Force-Directed Content Browsing
	13.5 Query by Example
	13.6 Query by Keywords
	13.7 Summary
	13.8 Exercises
	References
Appendix Deriving the Conditional Probability of a Gaussian Process
Index