Hierarchical semi-Markov conditional random fields for deep recursive sequential data

Tran, Truyen, Phung, Dinh, Bui, Hung and Venkatesh, Svetha 2017, Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Artificial intelligence, vol. 246, pp. 53-85, doi: 10.1016/j.artint.2017.02.003.

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Title Hierarchical semi-Markov conditional random fields for deep recursive sequential data
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Bui, Hung
Venkatesh, Svetha
Journal name Artificial intelligence
Volume number 246
Start page 53
End page 85
Total pages 33
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-05
ISSN 0004-3702
Keyword(s) deep nested sequential processes
hierarchical semi-Markov conditional
random field
partial labelling
constrained inference
numerical scaling
Summary We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.
Language eng
DOI 10.1016/j.artint.2017.02.003
Field of Research 0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092015

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