An Extension of Separable Lattice 2-D HMMs for Rotational Data Variations

Akira TAMAMORI  Yoshihiko NANKAKU  Keiichi TOKUDA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.8   pp.2074-2083
Publication Date: 2012/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.2074
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
Keyword: 
image recognition,  hidden Markov models,  variational method,  

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Summary: 
This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance. Therefore, the appropriate normalization processes for such variations are required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariance to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments among the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.