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PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

PhD Thesis Semi-Supervised Ensemble Methods for Computer

PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision Christian Leistner ————————————– Graz University of Technology Institute for Computer Graphics and Vision Thesis supervisors Prof. Dr. Horst Bischof Dr. Vincent Lepetit Graz, June 2010

  • Page 3: When we write programs that “lear
  • 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
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    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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    4.3. Experiments on Visual Classifi

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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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    5.2. On Robustness of On-line Boost

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    5.2. On Robustness of On-line Boost

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    5.2. On Robustness of On-line Boost

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    5.2. On Robustness of On-line Boost

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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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    6.1. Semi-Supervised Learning with

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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

  • Page 123 and 124:

    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

  • Page 153 and 154:

    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

  • Page 171 and 172:

    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

  • Page 177 and 178:

    BIBLIOGRAPHY 151 [Blum and Kalai, 1

  • Page 179 and 180:

    BIBLIOGRAPHY 153 [Duda et al., 2001

  • Page 181 and 182:

    BIBLIOGRAPHY 155 [Jain et al., 2008

  • Page 183 and 184:

    BIBLIOGRAPHY 157 [Long and Servedio

  • Page 185 and 186:

    BIBLIOGRAPHY 159 [Raina et al., 200

  • Page 187 and 188:

    BIBLIOGRAPHY 161 [Vandist et al., 2

  • Page 189:

    BIBLIOGRAPHY 163 [Zhu and Ghahraman

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