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Learning Marginalization through Regression for Hand Orientation Inference

Asad, M. & Slabaugh, G. G. (2016). Learning Marginalization through Regression for Hand Orientation Inference. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1215-1223. doi: 10.1109/CVPRW.2016.154


We present a novel marginalization method for multilayered Random Forest based hand orientation regression. The proposed model is composed of two layers, where the first layer consists of a marginalization weights regressor while the second layer contains expert regressors trained on subsets of our hand orientation dataset. We use a latent variable space to divide our dataset into subsets. Each expert regressor gives a posterior probability for assigning a given latent variable to the input data. Our main contribution comes from the regression based marginalization of these posterior probabilities. We use a Kullback-Leibler divergence based optimization for estimating the weights that are used to train our marginalization weights regressor. In comparison to the state-of-the-art of both hand orientation inference and multi-layered Random Forest marginalization, our proposed method proves to be more robust.

Publication Type: Article
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Engineering
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