PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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Statutory Declaration I declare that I have authored this thesis independently, that I have not used other than the declared sources / resources, and that I have explicitly marked all material which has been quoted either literally or by content from the used sources. Place Date Signature Eidesstattliche Erklärung Ich erkläre an Eides statt, dass ich die vorliegende Arbeit selbstständig verfasst, andere als die angegebenen Quellen/Hilfsmittel nicht benutzt, und die den benutzten Quellen wörtlich und inhaltlich entnommene Stellen als solche kenntlich gemacht habe. Ort Datum Unterschrift
Page 1: PhD Thesis Semi-Supervised Ensemble
Page 7 and 8: Acknowledgement To the god of thund
Page 9 and 10: Abstract Visual object classificati
Page 11 and 12: Kurzfassung Visuelle Objekterkennun
Page 13 and 14: Contents List of Figures List of Ta
Page 15 and 16: CONTENTS iii 6.1.5 Airbag . . . . .
Page 17 and 18: List of Figures 1.1 The eyes are th
Page 19 and 20: LIST OF FIGURES vii 6.2 Training a
Page 21 and 22: LIST OF FIGURES ix 9.15 Illustrativ
Page 23 and 24: List of Tables 5.1 Loss functions u
Page 25 and 26: List of Algorithms 2.1 AdaBoost [Fr
Page 27 and 28: Chapter 1 Introduction “W hat doe
Page 29 and 30: 3 1902]. Hence, computer vision has
Page 31 and 32: 5 ing algorithms. Additionally, in
Page 33 and 34: 1.2. Outline 7 has several benefits
Page 35 and 36: Chapter 2 Preliminaries and Notatio
Page 37 and 38: 2.2. Off-line versus On-line Learni
Page 39 and 40: 2.3. Ensemble Methods 13 and genera
Page 41 and 42: 2.3. Ensemble Methods 15 and adds i
Page 43 and 44: 2.4. On-line Boosting 17 Figure 2.1
Page 45 and 46: 2.4. On-line Boosting 19 Algorithm
Page 47 and 48: 2.4. On-line Boosting 21 Algorithm
Page 49 and 50: 2.4. On-line Boosting 23 2.4.2.1 Ra
Page 51 and 52: Chapter 3 Overview of Semi-Supervis
Page 53 and 54: 3.2. Why does SSL work? 27 unsuperv
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3.3. Self-Training 29 the same. The
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3.5. Co-Training and Multi-View Lea
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3.6. Graph-based Methods 33 multi-v
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3.6. Graph-based Methods 35 The dif
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3.7. Boosting and SSL 37 Kullback-L
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3.9. SSL from weakly related data 3
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3.10. Summary 41 Recently, Dai et a
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Chapter 4 SemiBoost and Visual Simi
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4.2. SemiBoost 45 following unlabel
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4.2. SemiBoost 47 Therefore, at eac
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4.2. SemiBoost 49 SemiBoost ? ? + -
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4.2. SemiBoost 51 Similar to the st
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4.3. Experiments on Visual Classifi
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4.4. Summary 59 (a) scene 1 (b) sce
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Chapter 5 On-line Semi-Supervised B
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5.1. On-line SemiBoost 63 approxima
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5.2. On Robustness of On-line Boost
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5.3. On-line SERBoost 77 (a) AdaBoo
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5.4. Machine Learning Experiments 7
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5.5. Summary and Conclusion 81 Data
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Chapter 6 Semi-Supervised Random Fo
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6.1. Semi-Supervised Learning with
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6.2. Prior Regularization 91 expect
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6.3. Experiments 93 Dataset # Train
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6.4. Summary 95 Figure 6.1: Improve
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Chapter 7 On-line Semi-Supervised R
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7.1. On-Line Random Forests 99 cord
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7.2. On-line Deterministic Annealin
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Chapter 8 Multiple Instance Learnin
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8.1. Related Work 105 mostly using
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8.3. MILForests 107 label. This mak
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8.4. On-line MILForests 109 In more
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8.5. Experiments 111 fidence output
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8.6. Summary 113 Figure 8.2: Some s
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Chapter 9 Visual Object Tracking In
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9.1. Tracking as a discriminative C
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9.2. An one-shot semi-supervised le
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9.2. An one-shot semi-supervised le
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9.3. Experiments 123 Figure 9.6: Sp
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9.3. Experiments 125 Figure 9.8: Th
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9.3. Experiments 127 on-line boosti
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9.3. Experiments 129 9.3.4 Benchmar
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9.3. Experiments 131 Figure 9.12: C
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9.3. Experiments 133 Quantitative C
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9.4. Summary 135 Figure 9.14: Illus
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Chapter 10 Conclusion “Learning h
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10.1. Discussion 139 methods should
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10.2. Outlook 141 e.g., may occur i
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Appendix A Publications My work at
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145 (21) Peter M. Roth, Helmut Grab
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Appendix B Acronyms DA Deterministi
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Bibliography [Abney, 2007] Abney, S
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BIBLIOGRAPHY 151 [Blum and Kalai, 1
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BIBLIOGRAPHY 153 [Duda et al., 2001
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BIBLIOGRAPHY 155 [Jain et al., 2008
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BIBLIOGRAPHY 157 [Long and Servedio
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BIBLIOGRAPHY 159 [Raina et al., 200
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BIBLIOGRAPHY 161 [Vandist et al., 2
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BIBLIOGRAPHY 163 [Zhu and Ghahraman
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PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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