Journal
PATTERN RECOGNITION
Volume 37, Issue 12, Pages 2281-2291Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(04)00162-1
Keywords
hidden Markov models; distance-based classification; 2D shape recognition; face classification; maximum-likelihood classification; matching pursuit
Ask authors/readers for more resources
Hidden Markov models (HMM) are a widely used tool for sequence modelling. In the sequence classification case, the standard approach consists of training one HMM for each class and then using a standard Bayesian classification rule. In this paper, we introduce a novel classification scheme for sequences based on HMMs, which is obtained by extending the recently proposed similarity-based classification paradigm to HMM-based classification. In this approach, each object is described by the vector of its similarities with respect to a predetermined set of other objects, where these similarities are supported by HMMs. A central problem is the high dimensionality of resulting space, and, to deal with it, three alternatives are investigated. Synthetic and real experiments show that the similarity-based approach outperforms standard HMM classification schemes. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available