IEICE TRANSACTIONS on Information
Efficient Cloth Pattern Recognition Using Random Ferns
Summary :
This paper proposes a novel image classification scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and efficient classification even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.475-478
- Publication Date
- 2015/02/01
- Publicized
- 2014/10/31
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8197
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Inseong HWANG
Yonsei University
Seungwoo JEON
Yonsei University
Beobkeun CHO
Yonsei University
Yoonsik CHOE
Yonsei University
Keyword
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Inseong HWANG, Seungwoo JEON, Beobkeun CHO, Yoonsik CHOE, "Efficient Cloth Pattern Recognition Using Random Ferns" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 475-478, February 2015, doi: 10.1587/transinf.2014EDL8197.
Abstract: This paper proposes a novel image classification scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and efficient classification even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8197/_p
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@ARTICLE{e98-d_2_475,
author={Inseong HWANG, Seungwoo JEON, Beobkeun CHO, Yoonsik CHOE, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Cloth Pattern Recognition Using Random Ferns},
year={2015},
volume={E98-D},
number={2},
pages={475-478},
abstract={This paper proposes a novel image classification scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and efficient classification even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.},
keywords={},
doi={10.1587/transinf.2014EDL8197},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Efficient Cloth Pattern Recognition Using Random Ferns
T2 - IEICE TRANSACTIONS on Information
SP - 475
EP - 478
AU - Inseong HWANG
AU - Seungwoo JEON
AU - Beobkeun CHO
AU - Yoonsik CHOE
PY - 2015
DO - 10.1587/transinf.2014EDL8197
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
AB - This paper proposes a novel image classification scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and efficient classification even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.
ER -
English
