Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition

An LIU; Maoyin CHEN; Donghua ZHOU

doi:10.1587/transinf.2014EDL8254

Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition

An LIU, Maoyin CHEN, Donghua ZHOU

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Summary :

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.5 pp.1116-1119

Publication Date: 2015/05/01

Publicized: 2015/02/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2014EDL8254

Type of Manuscript: LETTER

Category: Image Recognition, Computer Vision

Authors

An LIU
  Tsinghua University
Maoyin CHEN
  Tsinghua University
Donghua ZHOU
  Tsinghua University

Keyword

dictionary learning, discriminative, low-rank error, crater recognition

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An LIU, Maoyin CHEN, Donghua ZHOU, "Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 5, pp. 1116-1119, May 2015, doi: 10.1587/transinf.2014EDL8254.
Abstract: Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8254/_p

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@ARTICLE{e98-d_5_1116,
author={An LIU, Maoyin CHEN, Donghua ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition},
year={2015},
volume={E98-D},
number={5},
pages={1116-1119},
abstract={Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.},
keywords={},
doi={10.1587/transinf.2014EDL8254},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1116
EP - 1119
AU - An LIU
AU - Maoyin CHEN
AU - Donghua ZHOU
PY - 2015
DO - 10.1587/transinf.2014EDL8254
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2015
AB - Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.
ER -