Skip to main content
SpringerLink
Log in

Evaluation on the Cross-Domain Cloud Databases

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 463))

  • 63 Accesses

Abstract

The cross-domain ground-based cloud classification is of great significance in meteorological research, and there is no such study in this field to our knowledge. In this paper, we first introduce several representative classification methods (BoW model, LBP and CLBP), including their motivations and feature representations. Then we make an evaluation of these three methods on two cross-domain cloud databases (the CAS and CAA databases). Finally, experimental results show that it is essential to make further research on the issue.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Singh, M., Glennen, M.: Automated ground-based cloud recognition. Pattern Anal. Appl. 8(3), 258–271 (2005)

    Google Scholar 

  2. Liu, L., Sun, X., Chen, F., Zhao, S., Gao, T.: Cloud classification based on structure features of infrared images. J. Atmos. Oceanic Technol. 28(3), 410–417 (2011)

    Google Scholar 

  3. Liu, S., Wang, C., Xiao, B., Zhang, Z., Cao, X.: Tensor ensemble of ground-based cloud sequences: its modeling, classification, and synthesis. IEEE Geosci. Remote Sens. Lett. 10(5), 1190–1194 (2013)

    Google Scholar 

  4. Zhuo, W., Cao, Z., Xiao, Y.: Cloud classification of ground-based images using texture-structure features. J. Atmos. Oceanic Technol. 31(1), 79–92 (2014)

    Google Scholar 

  5. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: European Conference on Computer Vision (ECCV), pp. 213–226 (2010)

    Google Scholar 

  6. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2690–2697 (2013)

    Google Scholar 

  7. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Patt. Anal. Mach. Intell. (TPAMI) 24(7), 971–987 (2002)

    Google Scholar 

  8. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Google Scholar 

  9. http://www.vision.caltech.edu/Image-DataSets/Caltech256/ (2013)

  10. http://pascallin.ecs.soton.ac.uk/challenges/voc/ (2012)

  11. http://www.image-net.org/ (2013)

  12. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Google Scholar 

  13. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367 (2010)

    Google Scholar 

  14. Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. Workshop on Statistical Learning in Computer Vision (ECCV) 1(1–22), 1–2 (2004)

    Google Scholar 

  15. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1794–1801 (2009)

    Google Scholar 

  16. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S.: Action recognition using context-constrained linear coding. IEEE Sig. Process. Lett. 19(7), 439–442 (2012)

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant No. 61501327, No. 61711530240 and No. 61401309, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600, and No. 15JCQNJC01700, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001, and Doctoral Fund of Tianjin Normal University under Grant No. 5RL134 and No. 52XB1405.

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Zhong Zhang, Donghong Li, Wen Xiao & Shuang Liu

Authors
  1. Zhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donghong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Min Jia

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xuhong Feng

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Z., Li, D., Xiao, W., Liu, S. (2019). Evaluation on the Cross-Domain Cloud Databases. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_272

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6571-2_272

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6570-5

  • Online ISBN: 978-981-10-6571-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation