Compact Sparse Coding for Ground-Based Cloud Classification

Shuang LIU; Zhong ZHANG; Xiaozhong CAO

doi:10.1587/transinf.2015EDL8095

Compact Sparse Coding for Ground-Based Cloud Classification

Shuang LIU, Zhong ZHANG, Xiaozhong CAO

Full Text Views

0

Share
Cite this

Summary :

Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.11 pp.2003-2007

Publication Date: 2015/11/01

Publicized: 2015/08/17

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDL8095

Type of Manuscript: LETTER

Category: Pattern Recognition

Authors

Shuang LIU
  Tianjin Normal University
Zhong ZHANG
  Tianjin Normal University
Xiaozhong CAO
  China Meteorological Administration (CMA)

Keyword

ground-based cloud classification, sparse coding, compact sparse coding

Cite this

Copy

Shuang LIU, Zhong ZHANG, Xiaozhong CAO, "Compact Sparse Coding for Ground-Based Cloud Classification" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 11, pp. 2003-2007, November 2015, doi: 10.1587/transinf.2015EDL8095.
Abstract: Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8095/_p

Copy

@ARTICLE{e98-d_11_2003,
author={Shuang LIU, Zhong ZHANG, Xiaozhong CAO, },
journal={IEICE TRANSACTIONS on Information},
title={Compact Sparse Coding for Ground-Based Cloud Classification},
year={2015},
volume={E98-D},
number={11},
pages={2003-2007},
abstract={Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2015EDL8095},
ISSN={1745-1361},
month={November},}

Copy

TY - JOUR
TI - Compact Sparse Coding for Ground-Based Cloud Classification
T2 - IEICE TRANSACTIONS on Information
SP - 2003
EP - 2007
AU - Shuang LIU
AU - Zhong ZHANG
AU - Xiaozhong CAO
PY - 2015
DO - 10.1587/transinf.2015EDL8095
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2015
AB - Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.
ER -