Journal
PATTERN RECOGNITION
Volume 46, Issue 6, Pages 1569-1578Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2012.11.028
Keywords
Conditional random field; Sequence segmentation; Sequence classification
Funding
- EU FP7 USEFIL project (Unobtrusive Smart Environments for Independent Living) [288532]
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In this paper, we consider the problem of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. To effect this goal, we introduce a novel dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, whereas, on the second level, the whole observed sequences are classified into one of the available learned classes. These two procedures are conducted in a joint, synergetic fashion, thus optimally exploiting the information contained in the used model training sequences. Model training is conducted by means of an efficient likelihood maximization algorithm, and inference is based on the familiar Viterbi algorithm. We evaluate the efficacy of our approach considering a real-world application, and we compare its performance to popular alternatives. (c) 2012 Elsevier Ltd. All rights reserved.
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