Boosting-Based Face Detection and Adaptation (Synthesis Lectures on Computer Vision #2)
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| Author(s): Cha Zhang, Zhengyou Zhang Publisher: Morgan and Claypool Publishers, Year: 2010 ISBN: 160845133X,9781608451340,9781608451333 Description: Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. Table of Contents: A Brief Survey of the Face Detection Literature Cascade-based Real-Time Face Detection Multiple Instance Learning for Face Detection Detector Adaptation Other Applications Conclusions and Future Work |
| Table of contents : Preface……Page 11 Introduction……Page 13 The Integral Image……Page 14 AdaBoost Learning……Page 15 The Attentional Cascade Structure……Page 18 Feature Extraction……Page 19 Variations of the Boosting Learning Algorithm……Page 26 Other Learning Schemes……Page 34 Book Overview……Page 37 Cascade-based Real-Time Face Detection……Page 41 Soft-Cascade Training……Page 42 Fat Stumps……Page 46 Pruning Using the Final Classification……Page 48 Multiple Instance Pruning……Page 51 Experimental Results……Page 52 Multiple Instance Learning for Face Detection……Page 57 Noisy-OR MILBoost……Page 58 ISR MILBoost……Page 60 Application of MILBoost to Low Resolution Face Detection……Page 62 Multiple Category Boosting……Page 66 Probabilistic McBoost……Page 67 Winner-Take-All McBoost……Page 69 Experimental Results……Page 72 A Practical Multi-view Face Detector……Page 75 Detector Adaptation……Page 81 Detector Adaptation……Page 82 Taylor-Expansion-Based Adaptation……Page 83 Adaptation of Logistic Regression Classifier……Page 84 Direct Labels……Page 85 Similarity Labels……Page 86 Adaptation of Boosting Classifiers……Page 87 Discussions and Related Work……Page 88 Experimental Results……Page 89 Results on Direct Labels……Page 90 Results on Similarity Labels……Page 92 Introduction……Page 95 AdaBoosting LBP……Page 97 Boosted Multi-Task Learning……Page 99 Experimental Results……Page 102 Introduction……Page 106 Related Works……Page 107 Sound Source Localization……Page 108 Boosting-Based Multimodal Speaker Detection……Page 110 Merge of Detected Windows……Page 112 Alternative Speaker Detection Algorithms……Page 113 Experimental Results……Page 114 Conclusions and Future Work……Page 123 Bibliography……Page 125 Authors’ Biographies……Page 139 |
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