Machine Learning in Computer Vision

Foreword. Preface 1. INTRODUCTION. 1 Research Issues on Learning in Computer Vision. 2 Overview of the Book. 3 Contributions. 2. THEORY: PROBABILISTIC CLASSIFIERS. 1 Introduction. 2 Preliminaries and Notations. 3 Bayes Optimal Error and Entropy. 4 Analysis of Classification Error of Estimated (Mismatched)Distribution. 5 Density of Distributions. 6 Complex Probabilistic Models and Small Sample Effects. 7 Summary. 3. THEORY: GENERALIZATION BOUNDS. 1 Introduction. 2 Preliminaries. 3 A Margin Distribution Based Bound. 4 Analysis. 5 Summary. 4. THEORY: SEMI-SUPERVISED LEARNING. 1 Introduction.2 Properties of Classification. 3 Existing Literature. 4 Semi-supervised Learning Using Maximum Likelihood Estimation. 5 Asymptotic Properties of Maximum Likelihood Estimation with Labeled and Unlabeled Data. 6 Learning with Finite Data. 7 Concluding Remarks. 5. ALGORITHM: MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM. 1 Previous Work. 2 Mutual Information, Bayes Optimal Error, Entropy, and Conditional Probability. 3 Maximum Mutual Information HMMs. 4 Discussion. 5 Experimental Results. 6 Summary. 6. ALGORITHM: MARGIN DISTRIBUTION OPTIMIZATION. 1 Introduction. 2 A Margin Distribution Based Bound. 3 Existing Learning Algorithms. 4 The Margin Distribution Optimization (MDO) Algorithm. 5 Experimental Evaluation. 6 Conclusions. 7. ALGORITHM: LEARNING THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS. 1 Introduction. 2 Bayesian Network Classifiers. 3 Switching between Models: Naive Bayes and TAN Classifiers. 4 Learning the Structure of Bayesian Network Classifiers: Existing Approaches. 5 Classification Driven Stochastic Structure Search. 6 Experiments. 7 Should Unlabeled Data Be Weighed Differently? 8 Active Learning. 9 Concluding Remarks. 8. APPLICATION: OFFICE ACTIVITY RECOGNITION. 1 Context-Sensitive Systems. 2 Towards Tractable and Robust Context Sensing. 3 Layered Hidden Markov Models (LHMMs). 4 Implementation of SEER. 5 Experiments. 6 Related Representations. 7Summary. 9. APPLICATION: MULTIMODAL EVENT DETECTION. 1 Fusion Models: A Review. 2 A Hierarchical Fusion Model. 3 Experimental Setup, Features, and Results. 4 Summary. 10. APPLICATION: FACIAL EXPRESSION RECOGNITION. 1 Introduction. 2 Human Emotion Research. 3 Facial Expression Recognition System. 4 Experimental Analysis. 5 Discussion. 11. APPLICATION: BAYESIAN NETWORK CLASSIFIERS FOR FACE DETECTION. 1 Introduction. 2 Related Work. 3 Applying Bayesian Network Classifiers to Face Detection. 4 Experiments. 5 Discussion. References. Index.