IEICE TRANSACTIONS on Information
Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization
Summary :
Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.7 pp.1422-1425
- Publication Date
- 2015/07/01
- Publicized
- 2015/03/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8252
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Shuang LIU
Tianjin Normal University
Zhong ZHANG
Tianjin Normal University
Baihua XIAO
Chinese Academy of Sciences
Xiaozhong CAO
China Meteorological Administration (CMA)
Keyword
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Shuang LIU, Zhong ZHANG, Baihua XIAO, Xiaozhong CAO, "Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 7, pp. 1422-1425, July 2015, doi: 10.1587/transinf.2014EDL8252.
Abstract: Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8252/_p
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@ARTICLE{e98-d_7_1422,
author={Shuang LIU, Zhong ZHANG, Baihua XIAO, Xiaozhong CAO, },
journal={IEICE TRANSACTIONS on Information},
title={Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization},
year={2015},
volume={E98-D},
number={7},
pages={1422-1425},
abstract={Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2014EDL8252},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization
T2 - IEICE TRANSACTIONS on Information
SP - 1422
EP - 1425
AU - Shuang LIU
AU - Zhong ZHANG
AU - Baihua XIAO
AU - Xiaozhong CAO
PY - 2015
DO - 10.1587/transinf.2014EDL8252
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2015
AB - Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.
ER -
English
