PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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iv CONTENTS 10 Conclusion 137 10.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 10.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 A Publications 143 B Acronyms 147
List of Figures 1.1 The eyes are the most important senses for a frog. . . . . . . . . . . . . . 2 2.1 On-line boosting for feature selection [Grabner and Bischof, 2006] . . . . 17 3.1 Typical SSL assumption: Unlabeled data (grey) helps to change the decision boundary so that it falls into areas of low density (right). . . . . . . . 27 3.2 Graph-based semi-supervised learning: Given some labeled and unlabeled data (a), we can create a connected graph (b). Given this graph, the final solution looks as illustrated in (c). . . . . . . . . . . . . . . . . . 34 3.3 Examples for difficult to obtain images: alpaca (a) and mongoose (b) . . . 39 4.1 SemiBoost uses graph-based regularization in order to train a boosted classifier from both labeled and unlabeled data. . . . . . . . . . . . . . . 44 4.2 SemiBoost combined with a learned similarity measure from given labeled samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 SemiBoost combined with a learned similarity measure from given labeled samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4 The similarity between two samples x and x ′ is approximated by the difference of the responses from an a priori given classifier F P (·). . . . . . . 50 4.5 Toy Example 1: Positive and negative labeled (red and blue circles) and unlabeled samples (back crosses) are used for learning via “common” boosting (a) and using the proposed SemiBoosting approach (b) which additionally takes unlabeled data into account. . . . . . . . . . . . . . . . 53 4.6 Toy Example 2: The decision boundary of an “honest” prior (green) is “corrected” by a SemiBoost classifier (red) and the combined decision boundary (blue) is archived. . . . . . . . . . . . . . . . . . . . . . . . . 53 4.7 Some representative collected positive and negative input samples. . . . . 54 v
Page 1: PhD Thesis Semi-Supervised Ensemble
Page 5: Statutory Declaration I declare tha
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Page 10 and 11: learning. Finally, we hypothesize t
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Page 14 and 15: ii CONTENTS 3.6 Graph-based Methods
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44 Chapter 4. SemiBoost and Visual
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138 Chapter 10. Conclusion As many
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142 Chapter 10. Conclusion
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144 Chapter A. Publications (8) Mar
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146 Chapter A. Publications
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148 Chapter B. Acronyms SVM Support
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150 BIBLIOGRAPHY [Balcan et al., 20
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152 BIBLIOGRAPHY [Chapelle and Zien
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154 BIBLIOGRAPHY [Gall and Lempinsk
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156 BIBLIOGRAPHY [Leistner et al.,
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158 BIBLIOGRAPHY [Nigam et al., 200
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160 BIBLIOGRAPHY [Shalev-Shwartz, 2
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162 BIBLIOGRAPHY [Xu et al., 2009]
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PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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