A conditional random field-based model for joint sequence segmentation and classification

Authors:
Sotirios P. Chatzis;Dimitrios I. Kosmopoulos;Paul Doliotis
Affiliations:
Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Cyprus;Department of Computer Science, Rutgers University, 08854 NJ, USA and NCSR Demokritos, Institute of Informatics and Telecommunications, GR 15310, Greece;NCSR Demokritos, Institute of Informatics and Telecommunications, GR 15310, Greece and University of Texas at Arlington, Computer Science and Engineering, 76013 TX, USA
Venue:
Pattern Recognition
Year:
2013

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Abstract

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.