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

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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