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Statistical Learning and Pattern Analysis for Image and Video Processing

:	Nanning Zheng, Jianru Xue
:	Statistical Learning and Pattern Analysis for Image and Video Processing
:	Springer-Verlag
:	9781848823129
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 135.30
:

:	Anwendungs-Software
:	English

:	365
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Why are We Writing This Book? Visual data (graphical, image, video, and visualized data) affect every aspect of modern society. The cheap collection, storage, and transmission of vast amounts of visual data have revolutionized the practice of science, technology, and business. Innovations from various disciplines have been developed and applied to the task of designing intelligent machines that can automatically detect and exploit useful regularities (patterns) in visual data. One such approach to machine intelligence is statistical learning and pattern analysis for visual data. Over the past two decades, rapid advances have been made throughout the ?eld of visual pattern analysis. Some fundamental problems, including perceptual gro- ing,imagesegmentation, stereomatching, objectdetectionandrecognition and- tion analysis and visual tracking, have become hot research topics and test beds in multiple areas of specialization, including mathematics, neuron-biometry, and c- nition. A great diversity of models and algorithms stemming from these disciplines has been proposed. To address the issues of ill-posed problems and uncertainties in visual pattern modeling and computing, researchers have developed rich toolkits based on pattern analysis theory, harmonic analysis and partial differential eq- tions, geometry and group theory, graph matching, and graph grammars. Among these technologies involved in intelligent visual information processing, statistical learning and pattern analysis is undoubtedly the most popular and imp- tant approach, and it is also one of the most rapidly developing ?elds, with many achievements in recent years. Above all, it provides a unifying theoretical fra- work for intelligent visual information processing applications.

	Preface	5
	Why are We Writing This Book?	5
	Acknowledgments	8
	Contents	9
	Chapter 1 Pattern Analysis and Statistical Learning	15
	1.1 Introduction	15
	1.1.1 Statistical Pattern Recognition	16
	1.1.2 Pattern Theory	18
	1.2 Statistical Classification	20
	1.2.1 Feature Extraction and Selection	20
	1.2.2 Classifier	21
	1.3 Visual Pattern Representation	22
	1.3.1 The Curse of Dimensionality	23
	1.3.2 Dimensionality Reduction Techniques	23
	1.4 Statistical Learning	24
	1.4.1 Prediction Risk	25
	1.4.2 Supervised, Unsupervised, and Others	26
	1.5 Summary	28
	References	28
	Chapter 2 Unsupervised Learning for Visual Pattern Analysis	29
	2.1 Introduction 2.1.1 Unsupervised Learning	29
	2.1.2 Visual Pattern Analysis	30
	2.1.3 Outline	31
	2.2 Cluster Analysis	31
	2.3 Clustering Algorithms	35
	2.3.1 Partitional Clustering	35
	2.3.2 Hierarchical Clustering	44
	2.4 Perceptual Grouping	47
	2.4.1 Hierarchical Perceptual Grouping	47
	2.4.2 Gestalt Grouping Principles	49
	2.4.3 Contour Grouping	53
	2.4.4 Region Grouping	59
	2.5 Learning Representational Models for Visual Patterns	61
	2.6 Summary	62
	Appendix	62
	References	62
	Chapter 3 Component Analysis	64
	3.1 Introduction	64
	3.2 Overview of Component Analysis	67
	3.3 Generative Models	68
	3.3.1 Principal Component Analysis	68
	3.3.2 Nonnegative Matrix Factorization	79
	3.3.3 Independent Component Analysis	85
	3.4 Discriminative Models	89
	3.4.1 Linear Discriminative Analysis	89
	3.4.2 Oriented Component Analysis	92
	3.4.3 Canonical Correlation Analysis	92
	3.4.4 Relevant Component Analysis	94
	3.5 Standard Extensions of the Linear Model 3.5.1 Latent Variable Analysis	96
	3.5.2 Kernel Method	96
	3.6 Summary	96
	References	97
	Chapter 4 Manifold Learning	99
	4.1 Introduction	99
	4.2 Mathematical Preliminaries	103
	4.2.1 Manifold Related Terminologies	103
	4.2.2 Graph Related Terminologies	104
	4.3 Global Methods	106
	4.3.1 Multidimensional Scaling	106
	4.3.2 Isometric Feature Mapping	107
	4.3.3 Variants of the Isomap	108
	4.4 Local Methods	112
	4.4.1 Locally Linear Embedding	112
	4.4.2 Laplacian Eigenmaps	115
	4.4.3 Hessian Eigenmaps	119
	4.4.4 Diffusion Maps	121
	4.5 Hybrid Methods: Global Alignment of Local Models	125
	4.5.1 Global Coordination of Local Linear Models	125
	4.5.2 Charting a Manifold	127
	4.5.3 Local Tangent Space Alignment	129
	4.6 Summary	129
	Appendix	130
	References	130
	Chapter 5 Functional Approximation	132
	5.1 Introduction	132
	5.2 Modeling and Approximating the Visual Data	135
	5.2.1 On Statistical Analysis	136
	5.2.2 On Harmonic Analysis	137
	5.2.3 Issues of Approximation and Compression	138
	5.3 Wavelet Transform and Lifting Scheme 5.3.1 Wavelet Transform	140
	5.3.2 Constructing a Wavelet Filter Bank	141
	5.3.3 Lifting Scheme	143
	5.3.4 Lifting-Based Integer Wavelet Transform	144
	5.4 Optimal IntegerWavelet Transform	145
	5.5 Introducing Adaptability into the Wavelet Transform	147
	5.5.1 Curve Singularities in an Image	148
	5.5.2 Anisotropic Basis	148
	5.5.3 Adaptive Lifting-Based Wavelet	150
	5.6 Adaptive Lifting Structure	151
	5.6.1 Adaptive Prediction Filters	151
	5.6.2 Adaptive Update Filters	153
	5.7 Adaptive Directional Lifting Scheme	154
	5.7.1 ADL Framework	155
	5.7.2 Implementation of ADL	156
	5.8 Motion Compensation Temporal Filtering in Video Coding