APLSSVM: Hybrid Entropy Models for Image Retrieval

Li Jun-yi; Li Jian-hua; Zhu Jin-hua; Chen Xiao-hui

doi:doi:10.11648/j.ijiis.s.2015040202.13

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APLSSVM: Hybrid Entropy Models for Image Retrieval

Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 2-2)

Received: 8 November 2014 Accepted: 12 November 2014 Published: 5 May 2015

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Abstract

Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.

Published in	International Journal of Intelligent Information Systems (Volume 4, Issue 2-2) This article belongs to the Special Issue Content-based Image Retrieval and Machine Learning
DOI	10.11648/j.ijiis.s.2015040202.13
Page(s)	9-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Remote Sensing Image, L1 Norm, Active Learning, PLSSVM (Probability Least Squares Support Vector Machine), Hybrid Entropy

References

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[3]	HAMANAKA Y, SHINODA K, TSUTAOKA T, et al. Committee-based active learning for speech recognition [J]. IEICE Transactions on Information and Systems, 2011, 94(10):2015-2023.
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[5]	SUN Z C, LIU Z G, LIU S H, et al. Active learning with support vector machines in remotely sensed image classification[C]//QIU P H. YIU C. ZHANG H. et al. Proceedings of the 2nd International Congress on Image and Signal Processing. Piscataway: IEEE Computer Society. 2009: 1-6.
[6]	TUIA D. RATLE F. PACIFICI F. et al. Active learning methods for remote sensing image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(7): 2218-2232.
[7]	CHEN Y, HE Z. Blind separation using a class of new independence measures[C]//IEEE. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE Signal Process, 2003: 309-312.
[8]	LONG J, YIN J P, ZHU E. An active learning method based on most possible misclassification sampling using committee [J]. Lecture Notes in Computer Science. 2007. 4617: 104-113.
[9]	BRUZZONE L, PERSELLO C. Active learning for classification of remote sensing images[C]//IEEE. Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE In-corporated, 2009: 693-696.
[10]	GAO Y, WANG X S, CHENG Y H, et al. Fault diagnosis using a probability least squares support vector classification machine [J]. Mining Science and Technology, 2010, 20(6) : 917-921.
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[13]	JOHN R S. Integrated spatial and feature image systems: retrieval, analysis, and compression [D]. New York: Columbia University, 1997.
[14]	WEI L, SAURABH P. JAMES E F. et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1185-1198.

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APA Style

Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui. (2015). APLSSVM: Hybrid Entropy Models for Image Retrieval. International Journal of Intelligent Information Systems, 4(2-2), 9-14. https://doi.org/10.11648/j.ijiis.s.2015040202.13

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ACS Style

Li Jun-yi; Li Jian-hua; Zhu Jin-hua; Chen Xiao-hui. APLSSVM: Hybrid Entropy Models for Image Retrieval. Int. J. Intell. Inf. Syst. 2015, 4(2-2), 9-14. doi: 10.11648/j.ijiis.s.2015040202.13

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AMA Style

Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui. APLSSVM: Hybrid Entropy Models for Image Retrieval. Int J Intell Inf Syst. 2015;4(2-2):9-14. doi: 10.11648/j.ijiis.s.2015040202.13

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@article{10.11648/j.ijiis.s.2015040202.13,
  author = {Li Jun-yi and Li Jian-hua and Zhu Jin-hua and Chen Xiao-hui},
  title = {APLSSVM: Hybrid Entropy Models for Image Retrieval},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-2},
  pages = {9-14},
  doi = {10.11648/j.ijiis.s.2015040202.13},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040202.13},
  abstract = {Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.},
 year = {2015}
}

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TY  - JOUR
T1  - APLSSVM: Hybrid Entropy Models for Image Retrieval
AU  - Li Jun-yi
AU  - Li Jian-hua
AU  - Zhu Jin-hua
AU  - Chen Xiao-hui
Y1  - 2015/05/05
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040202.13
DO  - 10.11648/j.ijiis.s.2015040202.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 9
EP  - 14
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040202.13
AB  - Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.
VL  - 4
IS  - 2-2
ER  -

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Author Information

Li Jun-yi

School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai, China
Li Jian-hua

School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai, China
Zhu Jin-hua

College of Network Communication Zhejiang Yuexiu University of Foreign Languages, Zhe Jiang, China
Chen Xiao-hui

Information Engineering School, Yulin University, Yulin, Shanxi, China

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Plain Text BibTeX RIS

APA Style

Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui. (2015). APLSSVM: Hybrid Entropy Models for Image Retrieval. International Journal of Intelligent Information Systems, 4(2-2), 9-14. https://doi.org/10.11648/j.ijiis.s.2015040202.13

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ACS Style

Li Jun-yi; Li Jian-hua; Zhu Jin-hua; Chen Xiao-hui. APLSSVM: Hybrid Entropy Models for Image Retrieval. Int. J. Intell. Inf. Syst. 2015, 4(2-2), 9-14. doi: 10.11648/j.ijiis.s.2015040202.13

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AMA Style

Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui. APLSSVM: Hybrid Entropy Models for Image Retrieval. Int J Intell Inf Syst. 2015;4(2-2):9-14. doi: 10.11648/j.ijiis.s.2015040202.13

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@article{10.11648/j.ijiis.s.2015040202.13,
  author = {Li Jun-yi and Li Jian-hua and Zhu Jin-hua and Chen Xiao-hui},
  title = {APLSSVM: Hybrid Entropy Models for Image Retrieval},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-2},
  pages = {9-14},
  doi = {10.11648/j.ijiis.s.2015040202.13},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040202.13},
  abstract = {Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - APLSSVM: Hybrid Entropy Models for Image Retrieval
AU  - Li Jun-yi
AU  - Li Jian-hua
AU  - Zhu Jin-hua
AU  - Chen Xiao-hui
Y1  - 2015/05/05
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040202.13
DO  - 10.11648/j.ijiis.s.2015040202.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 9
EP  - 14
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040202.13
AB  - Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.
VL  - 4
IS  - 2-2
ER  -

Copy | Download