PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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58 Chapter 4. SemiBoost and Visual Similarity Learning Figure 4.11: Detection results of a state-of-the-art face detector (left) and the improved results obtained by the proposed strategy (right). As can be seen, incorporating additional unlabeled data helps to increase both recall and precision.
4.4. Summary 59 (a) scene 1 (b) scene 2 (c) improved results Figure 4.12: A scene specific car detector for scene 1 (a) is applied on a “similar” scene (b). The poor behavior can be significantly improved using unlabeled data taken from the second scene as shown by a typical frame (c). Figure 4.13: ROC curve for “scene 2” for the starting classifier (red) and the improved classifier (blue) using unlabeled samples from the target scene. sub-patches from the scene these 2000 serve as unlabeled examples to train a SemiBoost classifier with 30 weak classifiers using the classifier trained on scene 1 as prior. A typical frame superimposed with the detection result is shown in Figure 4.12(b)(c). The detection results improved (much lower false positive rate and higher detection rate) as shown in Figure 4.13. 4.4 Summary In this chapter, we introduced a boosting algorithm that combines semi-supervised learning with learning of distance metrics. Learning distance metrics is especially interesting for computer vision applications because for a given task it is often hard to say which metric to take. Distance functions can be learned using a small amount of data and can be used as prior information in order to support the exploitation of unlabeled data during a SSL process. We demonstrated the approach on several vision applications such face detection and visual transfer learning. In the following chapter, we will show how to extend
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PhD Thesis Semi-Supervised Ensemble
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Statutory Declaration I declare tha
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Most of all, I would like to thank
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learning. Finally, we hypothesize t
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sten Teil dieser Arbeit schlagen wi
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ii CONTENTS 3.6 Graph-based Methods
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iv CONTENTS 10 Conclusion 137 10.1
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vi LIST OF FIGURES 4.8 Performance
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viii LIST OF FIGURES 9.7 Comparison
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x LIST OF FIGURES
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xii LIST OF TABLES 8.2 Results and
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xiv LIST OF ALGORITHMS
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2 Chapter 1. Introduction Figure 1.
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4 Chapter 1. Introduction the liter
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6 Chapter 1. Introduction 1.1 Contr
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108 Chapter 8. Multiple Instance Le
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116 Chapter 9. Visual Object Tracki
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138 Chapter 10. Conclusion As many
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140 Chapter 10. Conclusion positive
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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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