IEICE TRANSACTIONS on Information
Two-Side Agreement Learning for Non-Parametric Template Matching
Summary :
We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.1 pp.140-149
- Publication Date
- 2017/01/01
- Publicized
- 2016/10/07
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7233
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Chao ZHANG
Iwate University
Takuya AKASHI
Iwate University
Keyword
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Chao ZHANG, Takuya AKASHI, "Two-Side Agreement Learning for Non-Parametric Template Matching" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 1, pp. 140-149, January 2017, doi: 10.1587/transinf.2016EDP7233.
Abstract: We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7233/_p
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@ARTICLE{e100-d_1_140,
author={Chao ZHANG, Takuya AKASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Two-Side Agreement Learning for Non-Parametric Template Matching},
year={2017},
volume={E100-D},
number={1},
pages={140-149},
abstract={We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.},
keywords={},
doi={10.1587/transinf.2016EDP7233},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Two-Side Agreement Learning for Non-Parametric Template Matching
T2 - IEICE TRANSACTIONS on Information
SP - 140
EP - 149
AU - Chao ZHANG
AU - Takuya AKASHI
PY - 2017
DO - 10.1587/transinf.2016EDP7233
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2017
AB - We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.
ER -
English
