By Antonio Criminisi, J Shotton
This functional and easy-to-follow textual content explores the theoretical underpinnings of choice forests, organizing the great present literature at the box inside a brand new, general-purpose wooded area version. issues and contours: with a foreword via Prof. Y. Amit and Prof. D. Geman, recounting their participation within the improvement of selection forests; introduces a versatile determination woodland version, in a position to addressing a wide and various set of picture and video research initiatives; investigates either the theoretical foundations and the sensible implementation of determination forests; discusses using choice forests for such initiatives as category, regression, density estimation, manifold studying, energetic studying and semi-supervised type; comprises workouts and experiments in the course of the textual content, with suggestions, slides, demo video clips and different supplementary fabric supplied at an linked web site; offers a loose, elementary software program library, permitting the reader to test with forests in a hands-on manner.
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Extra resources for Decision Forests for Computer Vision and Medical Image Analysis
7 with the only difference that now we set ρ = 5 as opposed to ρ = 500. Thus, much fewer separating lines/curves were made available to each node during training. This increases the randomness of each tree and reduces their correlation. Larger randomness reduces the blocky artifacts of the axis-aligned weak learner and produces more rounded decision boundaries (first column in Fig. 8). Furthermore, larger randomness yields a much lower overall confidence. This is noticeable especially in shallower trees (the washed-out colors in the top row).
The experiment in Fig. 6 illustrates this point. We are given four sets of well separated point clusters, one cluster per class. We train three forests on those points 34 A. Criminisi and J. Shotton Fig. 6 The effect of the weak learner model. (a) A four-class training set. (b) The testing posterior for a forest with axis-aligned weak learners. In regions far from the training points the posterior is overconfident (illustrated by saturated, rich colors). (c) The testing posterior for a forest with oriented line weak learners.
H(v, θ j ) = φ(v) > τ with φ(v) = x1 . e. the yellow point with largest x1 and the red point with smallest x1 . For a fixed value of x2 the classification forest produces the posterior p(c|x1 ) for the two classes c1 and c2 . The 3 Analogous to support vectors in SVM. 38 A. Criminisi and J. Shotton Fig. 9 Forest’s maximum margin properties. (a) Input 2-class training points. They are separated by a gap of dimension Δ. (b) Forest posterior. Note that all of the uncertainty band resides within the gap.