Extreme value theory based methods for visual recognition Preface Acknowledgments Figure Credits Extrema and Visual Recognition An Alternative to Central Tendency Modeling Background Extreme Value Theory for Recognition Decision Making in Machine Learning Organization A Brief Introduction to Statistical Extreme Value Theory Basic Concepts The Extreme Value Theorem Distributions in the EVT Family Gumbel Distribution Fréchet Distribution Weibull Distribution Generalized Extreme Value Distribution Rayleigh Distribution Generalized Pareto Distribution Tail Size Estimation The i.i.d. Assumption and Visual Data Post recognition Score Analysis Failure Prediction for Recognition Systems Meta recognition A Formal Model of Recognition Meta recognition as Hypothesis Testing Weibull based Meta recognition Validation Tools for Meta recognition Uses of Meta recognition for Visual Recognition Recognition Score Normalization Goals of Good Recognition Score Normalization W score Normalization Empirical Evaluation of W score Fusion Other Instantiations of EVT Normalization GEV based Normalization: Extreme Value Sample Consensus GEV based Normalization: GEV Kmeans Pareto based Normalization: Image Retrieval as Outlier Detection Pareto based Normalization: Visual Inspection via Anomaly Detection Calibration of Supervised Machine Learning Algorithms Goals of Calibration for Decision Making Probability of Exclusion: Multi attribute Spaces Open Set Recognition Probability of Inclusion: P_I SVM Probability of Inclusion and Exclusion: W SVM Sparse Representation based Open Set Recognition EVT Calibration for Deep Networks: OpenMax Summary and Future Directions Bibliography Authors Biography Blank Page