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Learning discriminative sequence models from partially labelled data for activity recognition

conference contribution
posted on 2008-01-01, 00:00 authored by T Truyen, H Bui, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh
Recognising daily activity patterns of people from low-level sensory data is an important problem. Traditional approaches typically rely on generative models such as the hidden Markov models and training on fully labelled data. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support fully supervised learning is often expensive. In this paper, we propose a new approach based on partially-supervised training of discriminative sequence models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that the approach can reduce labelling effort, and at the same time, provides us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart (i.e. the partially hidden Markov model), even when a substantial amount of labels are unavailable.

History

Event

Pacific Rim International Conference on Artificial Intelligence (10th : 2008 : Hanoi, Vietnam)

Source

PRICAI 2008 : trends in artificial intelligence : 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, December 15-19, 2008, proceedings

Series

Lecture notes in artificial intelligence ; 5351

Pagination

903 - 912

Publisher

Springer

Location

Hanoi, Vietnam

Place of publication

Berlin, Germany

Start date

2008-12-15

End date

2008-12-19

ISSN

0302-9743

ISBN-13

9783540891963

ISBN-10

354089196X

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2008, Springer

Extent

117

Editor/Contributor(s)

T Ho, Z Zhou

Title of proceedings

PRICAI 2008 : Trends in Artificial Intelligence : 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, December 15-19, 2008, proceedings