Download PDF by Antonio Criminisi, J Shotton: Decision Forests for Computer Vision and Medical Image

By Antonio Criminisi, J Shotton

ISBN-10: 1447149289

ISBN-13: 9781447149286

ISBN-10: 1447149297

ISBN-13: 9781447149293

This sensible and easy-to-follow textual content explores the theoretical underpinnings of choice forests, organizing the enormous present literature at the box inside a brand new, general-purpose wooded area version. subject matters and lines: with a foreword through Prof. Y. Amit and Prof. D. Geman, recounting their participation within the improvement of choice forests; introduces a versatile selection woodland version, able to addressing a wide and various set of photograph and video research projects; investigates either the theoretical foundations and the sensible implementation of determination forests; discusses using choice forests for such projects as category, regression, density estimation, manifold studying, lively studying and semi-supervised type; comprises workouts and experiments through the textual content, with strategies, slides, demo movies and different supplementary fabric supplied at an linked web site; presents a loose, hassle-free software program library, allowing the reader to scan with forests in a hands-on manner.

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4a and gray-ish ones in Fig. 4b , c ). This behavior is very much in agreement with our intuition of uncertainty. In this case we have employed a richer conic section weak learner model (see Sect. 3) which reduces the artifacts observed in the previous example and yields smoother posteriors. Notice for instance in Fig. 4b how the curve separating the red and the green spiral arms is nicely continued away from training points (with increasing uncertainty). If the noise in the position of training points increases (cf.

1 The Training Objective Function Forest training happens by optimizing the parameters of the weak learner at each split node j via: θ j = arg max I (Sj , θ ). 2) 28 A. Criminisi and J. Shotton with i indexing the two child nodes. 2) to avoid cluttering the notation. 3) c∈C where p(c) is calculated as the normalized empirical histogram of labels corresponding to the training points in S. As illustrated in Fig. 1b training a classification tree by maximizing the information gain has the tendency to produce trees where the entropy of the class distributions associated with the nodes decreases (the prediction confidence increases) when going from the root towards the leaves.

The color of tree nodes and edges indicates the class probability of training points going through them. (c) In testing, increasing the forest size T produces smoother class posteriors. 3 shows a first synthetic example. Training points belonging to two different classes (shown in yellow and red) are randomly drawn from two well separated Gaussian distributions (Fig. 3a). The points are represented as 2-vectors, where each dimension represents a different feature. A forest of shallow trees (D = 2) and varying size T is trained on those points.

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Decision Forests for Computer Vision and Medical Image Analysis by Antonio Criminisi, J Shotton

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