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Machine Learning in Computer Vision
von: Nicu Sebe, Ira Cohen, Ashutosh Garg
Springer-Verlag, 2005
ISBN: 9781402032752, 257 Seiten
Format: PDF, OL
Preis: 95,23 EUR
Contents
7
Foreword
13
Preface
15
1 INTRODUCTION
18
1. Research Issues on Learning in Computer Vision
19
2. Overview of the Book
23
3. Contributions
29
2 THEORY: PROBABILISTIC CLASSIFIERS
32
1. Introduction
32
2. Preliminaries and Notations
35
2.1 Maximum Likelihood Classification
35
2.2 Information Theory
36
2.3 Inequalities
37
3. Bayes Optimal Error and Entropy
37
4. Analysis of Classification Error of Estimated (Mismatched) Distribution
44
4.1 Hypothesis Testing Framework
45
4.2 Classification Framework
47
5. Density of Distributions
48
5.1 Distributional Density
50
5.2 Relating to Classification Error
54
6. Complex Probabilistic Models and Small Sample Effects
57
7. Summary
58
3 THEORY: GENERALIZATION BOUNDS
62
1. Introduction
62
2. Preliminaries
64
3. A Margin Distribution Based Bound
66
3.1 Proving the Margin Distribution Bound
66
4. Analysis
74
4.1 Comparison with Existing Bounds
76
5. Summary
81
4 THEORY: SEMI-SUPERVISED LEARNING
82
1. Introduction
82
2. Properties of Classification
84
3. Existing Literature
85
4. Semi-supervised Learning Using Maximum Likelihood Estimation
87
5. Asymptotic Properties of Maximum Likelihood Estimation with Labeled and Unlabeled Data
90
5.1 Model Is Correct
93
5.2 Model Is Incorrect
94
5.3 Examples: Unlabeled Data Degrading Performance with Discrete and Continuous Variables
97
5.4 Generating Examples: Performance Degradation with Univariate Distributions
100
5.5 Distribution of Asymptotic Classi.cation Error Bias
103
5.6 Short Summary
105
6. Learning with Finite Data
107
6.1 Experiments with Artificial Data
108
6.2 Can Unlabeled Data Help with Incorrect Models? Bias vs. Variance Effects and the Labeled-unlabeled Graphs
109
6.3 Detecting When Unlabeled Data Do Not Change the Estimates
114
6.4 Using Unlabeled Data to Detect Incorrect Modeling Assumptions
116
7. Concluding Remarks
117
5 ALGORITHM: MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM
120
1. Previous Work
120
2. Mutual Information, Bayes Optimal Error, Entropy, and Conditional Probability
122
3. Maximum Mutual Information HMMs
124
3.1 Discrete Maximum Mutual Information HMMs
125
3.2 Continuous Maximum Mutual Information HMMs
127
3.3 Unsupervised Case
128
4. Discussion
128
4.1 Convexity
128
4.2 Convergence
129
4.3 Maximum A-posteriori View of Maximum Mutual Information HMMs
129
5. Experimental Results
132
5.1 Synthetic Discrete Supervised Data
132
5.2 Speaker Detection
132
5.3 Protein Data
134
5.4 Real-time Emotion Data
134
6. Summary
134
6 ALGORITHM: MARGIN DISTRIBUTION OPTIMIZATION
136
1. Introduction
136
2. A Margin Distribution Based Bound
137
3. Existing Learning Algorithms
138
4. The Margin Distribution Optimization (MDO) Algorithm
142
4.1 Comparison with SVM and Boosting
143
4.2 Computational Issues
143
5. Experimental Evaluation
144
6. Conclusions
145
7 ALGORITHM: LEARNING THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS
146
1. Introduction
146
2. Bayesian Network Classifiers
147
2.1 Naive Bayes Classifiers
149
2.2 Tree-Augmented Naive Bayes Classifiers
150
3. Switching between Models: Naive Bayes and TAN Classifiers
155
4. Learning the Structure of Bayesian Network Classifiers: Existing Approaches
157
4.1 Independence-based Methods
157
4.2 Likelihood and Bayesian Score-based Methods
159
5. Classification Driven Stochastic Structure Search
160
5.1 Stochastic Structure Search Algorithm
160
5.2 Adding VC Bound Factor to the Empirical Error Measure
162
6. Experiments
163
6.1 Results with Labeled Data
163
6.2 Results with Labeled and Unlabeled Data
164
7. Should Unlabeled Data Be Weighed Differently?
167
8. Active Learning
168
9. Concluding Remarks
170
8 APPLICATION: OFFICE ACTIVITY RECOGNITION
174
1. Context-Sensitive Systems
174
2. Towards Tractable and Robust Context Sensing
176
3. Layered Hidden Markov Models (LHMMs)
177
3.1 Approaches
178
3.2 Decomposition per Temporal Granularity
179
4. Implementation of SEER
181
4.1 Feature Extraction and Selection in SEER
181
4.2 Architecture of SEER
182
4.3 Learning in SEER
183
4.4 Classification in SEER
183
5. Experiments
183
5.1 Discussion
186
6. Related Representations
187
7. Summary
189
9 APPLICATION: MULTIMODAL EVENT DETECTION
192
1. Fusion Models: A Review
193
2. A Hierarchical Fusion Model
194
2.1 Working of the Model
195
2.2 The Duration Dependent Input Output Markov Model
196
3. Experimental Setup, Features, and Results
199
4. Summary
200
10 APPLICATION: FACIAL EXPRESSION RECOGNITION
204
1. Introduction
204
2. Human Emotion Research
206
2.1 Affective Human-computer Interaction
206
2.2 Theories of Emotion
207
2.3 Facial Expression Recognition Studies
209
3. Facial Expression Recognition System
214
3.1 Face Tracking and Feature Extraction
214
3.2 Bayesian Network Classifiers: Learning the “Structure” of the Facial Features
217
4. Experimental Analysis
218
4.1 Experimental Results with Labeled Data
221
4.1.1 Person-dependent Tests
222
4.1.2 Person-independent Tests
223
4.2 Experiments with Labeled and Unlabeled Data
224
5. Discussion
225
11 APPLICATION: BAYESIAN NETWORK CLASSIFIERS FOR FACE DETECTION
228
1. Introduction
228
2. Related Work
230
3. Applying Bayesian Network Classifiers to Face Detection
234
4. Experiments
235
5. Discussion
239
References
242
Index
254
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