A Graphical Model Approach for Matching Partial Signatures

Xianzhi Du, David Doermann, Wael Abd-Almageed; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1465-1472

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.

Related Material


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[bibtex]
@InProceedings{Du_2015_CVPR,
author = {Du, Xianzhi and Doermann, David and Abd-Almageed, Wael},
title = {A Graphical Model Approach for Matching Partial Signatures},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}