Book overview. This book presents a unified, efficient model of decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images and automatic diagnosis from radiological scans. Such applications have traditionally been addressed by different, supervised or unsupervised machine learning techniques.

However, in this book, diverse learning tasks including regression, classification and semi-supervised learning are all seen as instances of the same general decision forest model. The unified framework further extends to novel uses of forests in tasks such as density estimation and manifold learning. This unification carries both theoretical and practical advantages. For instance, the underlying single model gives us the opportunity to implement and optimize the general algorithm for all these tasks only once, and then easily adapt it to individual applications with relatively small changes.

Part I describes the general forest model which unifies classification, regression, density estimation, manifold learning, semi-supervised learning and active learning under the same flexible framework. The proposed model may be used both in a discriminative or generative way and may be applied to discrete or continuous, labelled or unlabelled data. It is based on a conventional training-testing framework, with the training phase trying to optimize a well defined energy function. Tasks such as classification or density estimation, supervised or unsupervised problems can all be addressed by setting a specific model for the objective function as well as the output prediction function.

Part II is a collection of invited chapters. Here various researchers show how it is possible to build different applications on top of the general forest model. Kinect-based player segmentation, semantic segmentation of photographs and automatic diagnosis of brain lesions are amongst the many applications discussed here.

Part III presents implementation details, documentation for the provided research software library, and some concluding remarks.

• A. Criminisi, J. Shotton, and E. Konukoglu, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning. Foundations and Trends in Computer Graphics and Computer Vision. NOW Publishers. Vol.7: No 2-3, pp 81-227. 2012.
• A. Criminisi, J. Shotton, and E. Konukoglu, Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, no. MSR-TR-2011-114, 28 October 2011. (pdf, ~35 MB)