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 in a hands-on manner.
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- Gao W, Xu F and Zhou Z (2022). Towards convergence rate analysis of random forests for classification, Artificial Intelligence, 313:C, Online publication date: 1-Dec-2022.
- Zharmagambetov A and Carreira-Perpiñán M Semi-supervised learning with decision trees Proceedings of the 36th International Conference on Neural Information Processing Systems, (2392-2405)
- Ma J, Pan Q and Guo Y (2022). Depth-first random forests with improved Grassberger entropy for small object detection, Engineering Applications of Artificial Intelligence, 114:C, Online publication date: 1-Sep-2022.
- Ferjaoui R, Cherni M, Boujnah S, Kraiem N and Kraiem T (2022). Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images, Computer Methods and Programs in Biomedicine, 209:C, Online publication date: 1-Sep-2021.
- Shen W, Guo Y, Wang Y, Zhao K, Wang B and Yuille A (2021). Deep Differentiable Random Forests for Age Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43:2, (404-419), Online publication date: 1-Feb-2021.
- Zharmagambetov A and Carreira-Perpiñán M Smaller, more accurate regression forests using tree alternating optimization Proceedings of the 37th International Conference on Machine Learning, (11398-11408)
- Hehn T, Kooij J and Hamprecht F (2019). End-to-End Learning of Decision Trees and Forests, International Journal of Computer Vision, 128:4, (997-1011), Online publication date: 1-Apr-2020.
- Yu J and Blaschko M (2020). The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses, IEEE Transactions on Pattern Analysis and Machine Intelligence, 42:3, (735-748), Online publication date: 1-Mar-2020.
- Wang H, Tang Y, Jia Z and Ye F (2019). Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 24:4, (2955-2968), Online publication date: 1-Feb-2020.
- Sjöberg A, Gustavsson E, Koppisetty A and Jirstrand M Federated Learning of Deep Neural Decision Forests Machine Learning, Optimization, and Data Science, (700-710)
- Dong Y, Lin M, Yue J and Shi L (2019). A low-cost photorealistic CG dataset rendering pipeline for facial landmark localization, Multimedia Tools and Applications, 78:16, (22397-22420), Online publication date: 1-Aug-2019.
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- Kosov S, Shirahama K and Grzegorzek M (2019). Labeling of partially occluded regions via the multi-layer CRF, Multimedia Tools and Applications, 78:2, (2551-2569), Online publication date: 1-Jan-2019.
- Tang C, Garreau D and von Luxburg U When do random forests fail? Proceedings of the 32nd International Conference on Neural Information Processing Systems, (2987-2997)
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- Jampour M, Moin M, Yu L and Bischof H (2018). Mapping Forests, Journal of Mathematical Imaging and Vision, 60:2, (232-245), Online publication date: 1-Feb-2018.
- Qian C and Yang X (2018). An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image, Computer Methods and Programs in Biomedicine, 153:C, (19-32), Online publication date: 1-Jan-2018.
- Shen W, Zhao K, Guo Y and Yuille A Label distribution learning forests Proceedings of the 31st International Conference on Neural Information Processing Systems, (834-843)
- Noymanee J, Nikitin N and Kalyuzhnaya A (2017). Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin, Procedia Computer Science, 119:C, (288-297), Online publication date: 1-Dec-2017.
- Su S, Chen G, Cheng X and Bi R Deep supervised hashing with nonlinear projections Proceedings of the 26th International Joint Conference on Artificial Intelligence, (2786-2792)
- Asad M and Slabaugh G (2017). SPORE, Computer Vision and Image Understanding, 161:C, (114-129), Online publication date: 1-Aug-2017.
- Zhang J, Liang J and Hu H (2017). Multi-view texture classification using hierarchical synthetic images, Multimedia Tools and Applications, 76:16, (17511-17523), Online publication date: 1-Aug-2017.
- Nalbach O, Arabadzhiyska E, Mehta D, Seidel H and Ritschel T (2017). Deep Shading, Computer Graphics Forum, 36:4, (65-78), Online publication date: 1-Jul-2017.
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- Kainz P, Burgsteiner H, Asslaber M and Ahammer H (2017). Training echo state networks for rotation-invariant bone marrow cell classification, Neural Computing and Applications, 28:6, (1277-1292), Online publication date: 1-Jun-2017.
- Henderson C and Izquierdo E Scalable pattern retrieval from videos using a random forest index Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, (1-7)
- Ni D, Ji X, Wu M, Wang W, Deng X, Hu Z, Wang T, Shen D, Cheng J and Wang H (2017). Automatic cystocele severity grading in transperineal ultrasound by random forest regression, Pattern Recognition, 63:C, (551-560), Online publication date: 1-Mar-2017.
- Mahapatra D (2017). Semi-supervised learning and graph cuts for consensus based medical image segmentation, Pattern Recognition, 63:C, (700-709), Online publication date: 1-Mar-2017.
- Wang X, Yan G, Wang H, Fu J, Hua J, Wang J, Yang Y, Zhang G and Bao H (2017). Semantic annotation for complex video street views based on 2D3D multi-feature fusion and aggregated boosting decision forests, Pattern Recognition, 62:C, (189-201), Online publication date: 1-Feb-2017.
- Zeppelzauer M, Poier G, Seidl M, Reinbacher C, Schulter S, Breiteneder C and Bischof H (2016). Interactive 3D Segmentation of Rock-Art by Enhanced Depth Maps and Gradient Preserving Regularization, Journal on Computing and Cultural Heritage , 9:4, (1-30), Online publication date: 19-Dec-2016.
- Kim S, Kang S and Kim Y (2016). Anisotropic diffusion noise filtering using region adaptive smoothing strength, Journal of Visual Communication and Image Representation, 40:PA, (384-391), Online publication date: 1-Oct-2016.
- Mahapatra D (2016). Combining multiple expert annotations using semi-supervised learning and graph cuts for medical image segmentation, Computer Vision and Image Understanding, 151:C, (114-123), Online publication date: 1-Oct-2016.
- Ibrahim M and Carman M (2016). Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank, ACM Transactions on Information Systems, 34:4, (1-38), Online publication date: 14-Sep-2016.
- Tóth M, Ruskó L and Csébfalvi B (2016). Automatic recognition of anatomical regions in three-dimensional medical images, Computers in Biology and Medicine, 76:C, (120-133), Online publication date: 1-Sep-2016.
- Kontschieder P, Fiterau M, Criminisi A and Bulò S Deep neural decision forests Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, (4190-4194)
- Baum D and Titschack J Cavity and pore segmentation in 3D images with ambient occlusion Proceedings of the Eurographics / IEEE VGTC Conference on Visualization: Short Papers, (113-117)
- Haines T, Mac Aodha O and Brostow G (2016). My Text in Your Handwriting, ACM Transactions on Graphics, 35:3, (1-18), Online publication date: 2-Jun-2016.
- Shuai B, Zuo Z, Wang G and Wang B (2016). Scene Parsing With Integration of Parametric and Non-Parametric Models, IEEE Transactions on Image Processing, 25:5, (2379-2391), Online publication date: 1-May-2016.
- Tao L and Matuszewski B (2016). Is 2D Unlabeled Data Adequate for Recognizing Facial Expressions?, IEEE Intelligent Systems, 31:3, (19-29), Online publication date: 1-May-2016.
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- Valentin J, Vineet V, Cheng M, Kim D, Shotton J, Kohli P, Nießner M, Criminisi A, Izadi S and Torr P (2015). SemanticPaint, ACM Transactions on Graphics, 34:5, (1-17), Online publication date: 3-Nov-2015.
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- Peter L, Pauly O, Chatelain P, Mateus D and Navab N Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 - Volume 9349, (637-644)
- Lombaert H, Criminisi A and Ayache N Spectral Forests Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 - Volume 9349, (547-555)
- Hatt C, Speidel M and Raval A Hough Forests for Real-Time, Automatic Device Localization in Fluoroscopic Images Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 - Volume 9349, (307-314)
- LaViola J Context aware 3D gesture recognition for games and virtual reality ACM SIGGRAPH 2015 Courses, (1-61)
- Jitkrittum W, Gretton A, Heess N, Eslami S, Lakshminarayanan B, Sejdinovic D and Szabó Z Kernel-based Just-In-Time learning for passing expectation propagation messages Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, (405-414)
- Chan L, Hsieh C, Chen Y, Yang S, Huang D, Liang R and Chen B Cyclops Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, (3001-3009)
- Sharp T, Keskin C, Robertson D, Taylor J, Shotton J, Kim D, Rhemann C, Leichter I, Vinnikov A, Wei Y, Freedman D, Kohli P, Krupka E, Fitzgibbon A and Izadi S Accurate, Robust, and Flexible Real-time Hand Tracking Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, (3633-3642)
- Müller M, Helljesen L, Prevost R, Viola I, Nylund K, Gilja O, Navab N and Wein W Deriving anatomical context from 4D ultrasound Proceedings of the 4th Eurographics Workshop on Visual Computing for Biology and Medicine, (173-180)
- LaViola J An introduction to 3D gestural interfaces ACM SIGGRAPH 2014 Courses, (1-42)
- Fanello S, Keskin C, Izadi S, Kohli P, Kim D, Sweeney D, Criminisi A, Shotton J, Kang S and Paek T (2014). Learning to be a depth camera for close-range human capture and interaction, ACM Transactions on Graphics, 33:4, (1-11), Online publication date: 27-Jul-2014.
- Taylor S, Keskin C, Hilliges O, Izadi S and Helmes J Type-hover-swipe in 96 bytes Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (1695-1704)
- Tajbakhsh N, Gurudu S and Liang J A Classification-Enhanced Vote Accumulation Scheme for Detecting Colonic Polyps Proceedings of the 5th International Workshop on Abdominal Imaging. Computation and Clinical Applications - Volume 8198, (53-62)
- Han X Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation Proceedings of the 4th International Workshop on Machine Learning in Medical Imaging - Volume 8184, (17-24)
- Widmaier F, Kappler D, Schaal S and Bohg J Robot arm pose estimation by pixel-wise regression of joint angles 2016 IEEE International Conference on Robotics and Automation (ICRA), (616-623)
Index Terms
- Decision Forests for Computer Vision and Medical Image Analysis
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