NDLI: A graphical model approach for matching partial signatures

Content Provider	IEEE Xplore Digital Library
Author	Xianzhi Du Doermann, D. AbdAlmageed, W.
Copyright Year	2015
Description	Author affiliation: UMIACS, Univ. of Maryland, College Park, MD, USA (Xianzhi Du; Doermann, D.) \|\| Inf. Sci. Inst., Univ. of Southern California, Marina del Rey, CA, USA (AbdAlmageed, W.)
Abstract	In this paper, we present a novel partial signature matching method using graphical models. Shape context features are extracted from the contour of signatures to capture local variations, and K-means clustering is used to build a visual vocabulary from a set of reference signatures. To describe the signatures, supervised latent Dirichlet allocation is used to learn the latent distributions of the salient regions over the visual vocabulary and hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. Our work is evaluated on three datasets derived from the DS-I Tobacco signature dataset with clean signatures and the DS-II UMD dataset with signatures with different degradations. The results show the effectiveness of the approach for both the partial and full signature matching.
Starting Page	1465
Ending Page	1472
File Size	690011
Page Count	8
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298753
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Context Mathematical model Vocabulary Training Feature extraction Accuracy
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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