Learning Structured Outputs from Partial Labels using Forest Ensemble
Abstract
Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our algorithm can handle partial labelling, and thus is particularly attractive in practice where reliable labels are often sparsely observed. In addition, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to an indoor video surveillance scenario, where activities are modelled at multiple levels.
Cite
@article{arxiv.1407.6432,
title = {Learning Structured Outputs from Partial Labels using Forest Ensemble},
author = {Truyen Tran and Dinh Phung and Svetha Venkatesh},
journal= {arXiv preprint arXiv:1407.6432},
year = {2014}
}
Comments
Conference version appeared in Truyen et al, AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition. CVPR'06