This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests ina hands-on manner.
Table of Content
Overview and Scope.- Notation and Terminology.- Part I: The Decision Forest Model. – Introduction.- Classification Forests.- Regression Forests.- Density Forests.- Manifold Forests.- Semi-Supervised Classification Forests.- Part II: Applications in Computer Vision and Medical Image Analysis. – Keypoint Recognition Using Random Forests and Random Ferns.- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.- Class-Specific Hough Forests for Object Detection.- Hough-Based Tracking of Deformable Objects.- Efficient Human Pose Estimation from Single Depth Images.- Anatomy Detection and Localization in 3D Medical Images.- Semantic Texton Forests for Image Categorization and Segmentation.- Semi-Supervised Video Segmentation Using Decision Forests.- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI.- Manifold Forests for Multi-Modality Classification of Alzheimer’s Disease.- Entangled Forests and Differentiable Information Gain Maximization.- Decision Tree Fields.- Part III: Implementation and Conclusion. – Efficient Implementation of Decision Forests.- The Sherwood Software Library.- Conclusions.
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