PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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vi LIST OF FIGURES 4.8 Performance of the similarity over the iterations. . . . . . . . . . . . . . . 55 4.9 Learning of a car-detector: Performance of the proposed approach improves significantly compared to the common approach (no unlabeled data is used) both when (a) including more weak classifiers and (b) use more unlabeled data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.10 Detection results of a face detector (a) which serves as prior to build a SemiBoost classifier using additional unlabeled data. This classifier alone (b) has not the power of delivering good results but the combination improves the result essentially. . . . . . . . . . . . . . . . . . . . . . . . . 57 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. 58 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). . . . . . . . 59 4.13 ROC curve for “scene 2” for the starting classifier (red) and the improved classifier (blue) using unlabeled samples from the target scene. . . . . . . 59 5.1 Different loss functions used in boosting and supervised machine learning methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.2 Comparison of common gradient descent (a) and functional gradient descent (b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3 Illustration of Gradient Boosting. . . . . . . . . . . . . . . . . . . . . . . 70 5.4 Weight update functions for different loss functions. . . . . . . . . . . . . 72 5.5 Results of the machine learning experiments when (a) stumps and (b) histograms are used as weak learners: test error is shown with respect to the label noise level. Classifiers: AdaBoost (blue), Real (green), Logit (red), DoomII (cyan), Savage (magenta). . . . . . . . . . . . . . . . . . . 75 5.6 Different number of wins for the machine learning experiments when (a) stumps and (b) histograms are used as weak learners: test error is shown with respect to the label noise level. Classifiers: AdaBoost (blue), Real (green), Logit (red), DoomII (cyan), Savage (magenta). . . . . . . . . . . 76 5.7 Co-training using (a) the non-robust on-line AdaBoost algorithm and (b) the robust On-line GradientBoost algorithm. . . . . . . . . . . . . . . . . 77 6.1 Improved average performance over the iterations for the multi-class problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
LIST OF FIGURES vii 6.2 Training a DAS-RF on corrupted unlabeled data (green) and its OOBE (green dashed) and on not corrupted data (blue). After 6 iterations the SSL stops and it is trained only on labeled data. Self-learning is depicted in red. . . . . . . . . . 96 8.1 Multiple instance learning principle: Positive bags (blue regions) consist of both positive and negative instances; however, the “real” instance labels are unknown to the learner. By contrast, in negative bags (red) all instances are guaranteed to be negative. . . . . . . . . . . . . . . . . . . 104 8.2 Some samples of the 20 COREL categories and their corresponding segmentations [Chen et al., 2006]. . . . . . . . . . . . . . . . . . . . . . . . 113 9.1 The original on-line AdaBoost tracking loop as proposed by [Grabner and Bischof, 2006]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 9.2 Tracking of a textured patch with difficult background (same texture). As soon as the object becomes occluded the original tracker from [Grabner and Bischof, 2006] (dotted cyan), drifts away. Our proposed methods (yellow) successfully re-detects the object and continues tracking. . . . . 117 9.3 The semi-supervised tracking loop: as can be seen, the on-line classifier is “aware” that putative update patches are unlabeled data, which are incorporated using prior knowledge in form of a static classifier. . . . . . . 119 9.4 Detection and tracking in principle can be viewed as the same problem, depending on how fast the classifier adapts to the current scene. On the one side a general object detector (e.g., [Viola and Jones, 2002b]) is located and on the other side a highly adaptive tracker (e.g., [Grabner and Bischof, 2006]). Our approach is somewhere in between, benefiting from both approaches: (i) be sufficiently adaptive to new appearance and lightning changes, and the simplification of object vs. background and (ii) limit (avoid large) drifting by keeping prior information from the object. 119 9.5 Dependency graph between neighboring samples of current and past frames (a) and (b) similarity matrix encoding this relation for 3 frames (block structure due to the grouping of positive and negative samples). . . . . . . 121 9.6 Spatial temporal coherence between samples in tracking: Thick points are samples from the current frame, thin circles are samples from previous frames. (left) classification from appearance based prior, (right) the smiliarity encoding S smoothes of individual (wrong) predictions. Color encoded is the probability of samples belonging to the positive class. . . . 123
Page 1: PhD Thesis Semi-Supervised Ensemble
Page 5: Statutory Declaration I declare tha
Page 8 and 9: Most of all, I would like to thank
Page 10 and 11: learning. Finally, we hypothesize t
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Page 14 and 15: ii CONTENTS 3.6 Graph-based Methods
Page 16 and 17: iv CONTENTS 10 Conclusion 137 10.1
Page 20 and 21: viii LIST OF FIGURES 9.7 Comparison
Page 22 and 23: x LIST OF FIGURES
Page 24 and 25: xii LIST OF TABLES 8.2 Results and
Page 26 and 27: xiv LIST OF ALGORITHMS
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42 Chapter 3. Overview of Semi-Supe
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44 Chapter 4. SemiBoost and Visual
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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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PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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