Learning Structured Outputs from Partial Labels using Forest Ensemble

Learning Structured Outputs from Partial Labels using Forest Ensemble Truyen Tran, Dinh Phung, Svetha V enkatesh Centre for Pattern Recognition and Data Analytics Deakin University, Australia 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. 1 Introduction There has been a growing research interest in developing probabilistic temporal graphical models for recognising human activities from sensory data. In this paper we address an important aspect of the problem in that there are multiple levels of abstraction, that is, an activity is often composed of several sub-activities. A popular approach to deal with such a hierarchical nature is to build a cascaded model: each level is modelled separately, and the output of the lower levels is subsequently used as the input for the upper levels [20]. This approach is sub-optimal because the information at the higher level is often very discriminative to infer about the lower levels, but it is not modelled. Moreover, the layered approach often suffers from the so-called cascading error problem, as the error introduced from the lower level will propagate to higher tasks. A better and more holistic approach is to build a joint representation at all layers. Emerging methods include generative/directed models such as abstract hidden Markov models (AH-MMs) [4], hierarchical HMMs [19], dynamic Bayesian networks [10], and their discriminative/undirected counterparts such as hierarchical conditional random field (HCRF) [17], and dynamic CRF (DCRF) [28].