Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing)

484 Pages·2007·5.78 MB·English

by  Francesco Camastra

Advanced Information and Knowledge Processing
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Professor Lakhmi Jain
[email protected]
Professor Xindong Wu
[email protected]
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Francesco Camastra • Alessandro Vinciarelli
Machine Learning for Audio,
Image and Video Analysis
Theory and Applications
ABC
Francesco Camastra, PhD Alessandro Vinciarelli, PhD
Polo Universitario Guglielmo Marconi, IDIAP Research Institute, Martigny,
University of Pisa, Italy Switzerland
AI&KP ISSN 1610-3947
ISBN: 978-1-84800-006-3 e-ISBN: 978-1-84800-007-0
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Control Number: 2007932413
© Springer-Verlag London Limited 2008
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted
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To our parents and families
Contents
1 Introduction ............................................... 1
1.1 Two Fundamental Questions ............................ 1
1.1.1 Why Should One Read The Book?................ 1
1.1.2 What Is the Book About? ....................... 2
1.2 The Structure of the Book .............................. 4
1.2.1 Part I: From Perception to Computation........... 4
1.2.2 Part II: Machine Learning ....................... 5
1.2.3 Part III: Applications ........................... 6
1.2.4 Appendices .................................... 7
1.3 How to Read This Book ................................ 7
1.3.1 Background and Learning Objectives.............. 8
1.3.2 Difficulty Level ................................. 8
1.3.3 Problems ...................................... 8
1.3.4 Software....................................... 9
1.4 Reading Tracks........................................ 9
Part I From Perception to Computation
2 Audio Acquisition, Representation and Storage ............ 13
2.1 Introduction .......................................... 13
2.2 Sound Physics, Production and Perception ................ 15
2.2.1 Acoustic Waves Physics ......................... 15
2.2.2 Speech Production.............................. 18
2.2.3 Sound Perception............................... 20
2.3 Audio Acquisition...................................... 22
2.3.1 Sampling and Aliasing........................... 23
2.3.2 The Sampling Theorem**........................ 25
2.3.3 Linear Quantization............................. 27
2.3.4 Nonuniform Scalar Quantization.................. 30
2.4 Audio Encoding and Storage Formats .................... 32
VIII Contents
2.4.1 Linear PCM and Compact Discs.................. 32
2.4.2 MPEG Digital Audio Coding..................... 34
2.4.3 AAC Digital Audio Coding ...................... 35
2.4.4 Perceptual Coding .............................. 36
2.5 Time-Domain Audio Processing ......................... 38
2.5.1 Linear and Time-Invariant Systems ............... 38
2.5.2 Short-Term Analysis ............................ 40
2.5.3 Time-Domain Measures ......................... 41
Problems.................................................... 46
References..................................................... 49
3 Image and Video Acquisition, Representation and Storage . 51
3.1 Introduction .......................................... 51
3.2 Human Eye Physiology ................................. 52
3.2.1 Structure of the Human Eye ..................... 52
3.3 Image Acquisition Devices .............................. 54
3.3.1 Digital Camera................................. 54
3.4 Color Representation................................... 57
3.4.1 Human Color Perception ........................ 57
3.4.2 Color Models................................... 59
3.5 Image Formats ........................................ 66
3.5.1 Image File Format Standards..................... 66
3.5.2 JPEG Standard ................................ 68
3.6 Video Principles ....................................... 72
3.7 MPEG Standard....................................... 73
3.7.1 Further MPEG Standards ....................... 75
3.8 Conclusions ........................................... 77
Problems.................................................... 78
References..................................................... 79
Part II Machine Learning
4 Machine Learning.......................................... 83
4.1 Introduction .......................................... 83
4.2 Taxonomy of Machine Learning.......................... 84
4.2.1 Rote Learning.................................. 84
4.2.2 Learning from Instruction........................ 85
4.2.3 Learning by Analogy ............................ 85
4.3 Learning from Examples................................ 85
4.3.1 Supervised Learning ............................ 86
4.3.2 Reinforcement Learning ......................... 86
4.3.3 Unsupervised Learning .......................... 87
Contents IX
4.4 Conclusions ........................................... 88
References..................................................... 89
5 Bayesian Theory of Decision ............................... 91
5.1 Introduction .......................................... 91
5.2 Bayes Decision Rule.................................... 92
5.3 Bayes Classifier(cid:1) ....................................... 95
5.4 Loss Function ......................................... 96
5.4.1 Binary Classification ............................ 98
5.5 Zero-One Loss Function ................................ 99
5.6 Discriminant Functions .................................100
5.6.1 Binary Classification Case .......................101
5.7 Gaussian Density ......................................101
5.7.1 Univariate Gaussian Density .....................102
5.7.2 Multivariate Gaussian Density....................102
5.7.3 Whitening Transformation .......................104
5.8 Discriminant Functions for Gaussian Likelihood............106
5.8.1 Features Are Statistically Independent ............106
5.8.2 Covariance Matrix Is The Same for All Classes .....107
5.8.3 Covariance Matrix Is Not the Same for All Classes ..109
5.9 Receiver Operating Curves..............................109
5.10 Conclusions ...........................................111
Problems....................................................112
References.....................................................115
6 Clustering Methods ........................................117
6.1 Introduction ..........................................117
6.2 Expectation and Maximization Algorithm(cid:1)................119
6.2.1 Basic EM(cid:1) .....................................120
6.3 Basic Notions and Terminology ..........................122
6.3.1 Codebooks and Codevectors......................122
6.3.2 Quantization Error Minimization .................124
6.3.3 Entropy Maximization...........................124
6.3.4 Vector Quantization.............................125
6.4 K-Means..............................................127
6.4.1 Batch K-Means.................................128
6.4.2 Online K-Means ................................129
6.4.3 K-Means Software Packages......................132
6.5 Self-Organizing Maps...................................132
6.5.1 SOM Software Packages .........................134
6.5.2 SOM Drawbacks................................134
6.6 Neural Gas and Topology Representing Network ...........134
6.6.1 Neural Gas ....................................135
X Contents
6.6.2 Topology Representing Network ..................135
6.6.3 Neural Gas and TRN Software Package............137
6.6.4 Neural Gas and TRN Drawbacks .................137
6.7 General Topographic Mapping(cid:1)..........................137
6.7.1 Latent Variables(cid:1) ...............................137
6.7.2 Optimization by EM Algorithm(cid:1)..................139
6.7.3 GTM versus SOM(cid:1) .............................140
6.7.4 GTM Software Package..........................141
6.8 Fuzzy Clustering Algorithms ............................141
6.8.1 FCM..........................................142
6.9 Hierarchical Clustering .................................142
6.10 Conclusion............................................144
Problems....................................................145
References.....................................................147
7 Foundations of Statistical Learning
and Model Selection .......................................149
7.1 Introduction ..........................................149
7.2 Bias-Variance Dilemma.................................150
7.2.1 Bias-Variance Dilemma for Regression.............150
7.2.2 Bias-Variance Decomposition for Classification(cid:1) ....151
7.3 Model Complexity .....................................153
7.4 VC Dimension and Structural Risk Minimization ..........156
7.5 Statistical Learning Theory(cid:1) ............................159
7.5.1 Vapnik-Chervonenkis Theory.....................161
7.6 AIC and BIC Criteria ..................................163
7.6.1 Akaike Information Criterion.....................163
7.6.2 Bayesian Information Criterion ...................164
7.7 Minimum Description Length Approach ..................165
7.8 Crossvalidation ........................................166
7.8.1 Generalized Crossvalidation ......................166
7.9 Conclusion............................................168
Problems....................................................168
References.....................................................171
8 Supervised Neural Networks
and Ensemble Methods ....................................173
8.1 Introduction ..........................................173
8.2 Artificial Neural Networks and Neural Computation........174
8.3 Artificial Neurons......................................175
8.4 Connections and Network Architectures ..................179
8.5 Single-Layer Networks..................................180