Skip to main content
SpringerLink
Log in

A color constancy based flower classification method in the blockchain data lake

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The efficient classification of flower images will directly affect the accuracy of their automatic recognition. Due to the complexity of the background of flowers, not only the color, shape and texture of flowers are different, but also the illumination factors show significant effect on classification results of flower images during the process of acquiring flower images. Therefore, it is of great practical significance to identify flowers with the help of flower salient features and eliminate lighting factors. In order to reduce the influence of illumination factor on the classification accuracy of flower images and ensure the true transparency of flower images in the process of Internet data transmission, in this paper, we propose a color constancy based flower classification method in the Blockchain Data Lake, short for CCAN, firstly, we design a Blockchain Data Lake framework to ensure the accuracy and originality of the original image data; and then, color constancy mechanism is used to encode the color feature of images, in order to reduce the illumination effects. Thirdly, a convolutional neural network based classifier is proposed to achieve flower classification. Finally, we simulate the performance of CCAN on three different data set in the blockchain Data Lake environment, extensive results show that the proposed CCAN effectively improves the accuracy of flower image classification by minimizing the interference of illumination factors on flower targets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Cheng K, Tan X (2014) Sparse representations based attribute learning for flower classification. Neurocomputing 145:416–426. https://doi.org/10.1016/j.neucom.2014.05.011

    Article  Google Scholar 

  2. Ansari P, Uddin MJ, Akther S, Azam S, Mahmud MK, Azad S, Hannan J (2017) Investigation of antinociceptive activity of methanolic extract of persicaria orientalis leaves in rodents. J Basic Clin Physiol Pharmacol 28:171–179. https://doi.org/10.1515/jbcpp-2016-0018

    Article  CAS  PubMed  Google Scholar 

  3. Akimov M, Luk’ yanchuk I, Zhbanova E, Lyzhin A (2020) Strawberry fruit (fragaria ananassa duch.) as a valuable source of nutritional and bio-logically active substances (review). Chemistry of Plant Raw Material, 5–18. https://doi.org/10.14258/jcprm.2020015511

  4. Brohi M (2021) Integration of iot and blockchain. Technium Romanian Journal of Applied Sciences and Technology 3: 32–41. https://doi.org/10.47577/technium.v3i8.4692

  5. Hasan MT, Miraz M, Sumi F, Sarkar S (2021) A blockchain research review. 07:26–35. https://doi.org/10.18488/journal.89.2021.71.26.35

  6. Nathan S, Govindarajan C, Saraf A, Sethi M, Jayachandran P (2019) Blockchain meets database: design and implementation of a blockchain relational database. Proceedings of the VLDB Endowment 12: 1539–1552. https://doi.org/10.14778/3342263.3342632

  7. Stawicki S, Firstenberg M, Papadimos T (2018) What’s new in academic medicine blockchain technology in health-care: bigger, better, fairer, faster, and leaner. International Journal of Academic Medicine 4:1–11. https://doi.org/10.4103/IJAM.IJAM_12_18

    Article  Google Scholar 

  8. Vangala A, Das AK, Kumar N, Alazab M (2020) Smart secure sensing for iot-based agriculture: blockchain perspective. IEEE Sensors J pp 1–1. https://doi.org/10.1109/JSEN.2020.3012294

  9. Ren P, Li S, Hou W, Zheng W, Li Z, Cui Q, Chang W, Li X, Zeng C, Sheng M, Zhang Y (2021) MHDP: an efficient data lake platform for medical multi-source heterogeneous data, pp 727–738. https://doi.org/10.1007/978-3-030-87571-8_63

  10. Panwar A, Bhatnagar V (2020) Scrutinize the idea of Hadoop-based data lake for big data storage, pp 365–391. https://doi.org/10.1007/978-981-15-3357-0_24

  11. Ouellette P, Sciortino A, Nargesian F, Ghadiri Bashardoost B, Zhu E, Pu K, Miller R (2021) Ronin: data lake exploration. Proceedings of the VLDB Endowment 14:2863–2866. https://doi.org/10.14778/3476311.3476364

  12. Rocca L, Veneziani M, Teodori C, Kopylova M (2021) Blockchain, pp 147–157. https://doi.org/10.1007/978-3-030-80737-5_11

  13. Nawaz A (2021) Blockchain of things (bcot): data management using blockchain technology. PhD thesis (September 2021). https://doi.org/10.13140/RG.2.2.32733.54243/1

  14. Mhaisen N, Fetais N, Erbad A, Mohamed A, Guizani M (2020) To chain or not to chain: A reinforcement learning approach for blockchain-enabled iot monitoring applications. Futur Gener Comput Syst 111

  15. Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Nawaz SA, Bhatti MA, Ain QU, Mehmood A (2022) Local similarity-based spatial-spectral fusion hyperspectral image classification with deep cnn and gabor filtering. IEEE Trans Geosci Remote Sens 60:1–15. https://doi.org/10.1109/TGRS.2021.3090410

    Article  Google Scholar 

  16. Huang M, Huang S, Zhang Y, Bhatti U (2020) Medical image segmentation using deep learning with feature enhancement. IET Image Process 14(5)

  17. Davis J, Pensky M (2014) Model selection for classification with a large number of classes. Springer Proceedings in Mathematics and Statistics 74:251–257

    MathSciNet  Google Scholar 

  18. Angelova A, Zhu S (2013) Efficient object detection and segmentation for fine-grained recognition. In: Computer vision and pattern recognition (CVPR), 2013 IEEE conference on, pp 811–818

  19. Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M (2022) Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput Biol Med 147:105766. https://doi.org/10.1016/j.compbiomed.2022.105766

    Article  CAS  PubMed  Google Scholar 

  20. Rostami M, Berahmand K, Nasiri E, Forouzandeh S (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100:104210. https://doi.org/10.1016/j.engappai.2021.104210

    Article  Google Scholar 

  21. Nasiri E, Berahmand K, Li Y (2022) Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks. Multimedia Tools and Applications 82:1–24. https://doi.org/10.1007/s11042-022-12943-8

    Article  Google Scholar 

  22. Yuan P, Li W, Ren S, Xu H (2018) Recognition for flower type and variety of chrysanthemum with convolutional neural network. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 34:152–158. https://doi.org/10.11975/j.issn.1002-6819.2018.05.020

  23. Wang D, Shen Z, Shao J, Zhang W, Xue X, Zhang Z (2015) Multiple granularity descriptors for fine-grained categorization, pp 2399–2406. https://doi.org/10.1109/ICCV.2015.276

  24. Wang X, Hu Q, Zhang Y, Zhang G, Juan W, Xing C (2018) A kind of decision model research based on big data and blockchain in eHealth: 15th international conference, WISA 2018, Taiyuan, China, September 14-15, 2018, proceedings, pp 300–306

  25. Gimenez-Aguilar M, Fuentes J, Gonzalez-Manzano L, Arroyo D (2021) Achieving cybersecurity in blockchain-based systems: a survey. Futur Gener Comput Syst 124(6)

  26. Puri V, Priyadarshini I, Kumar R, Le C (2021) Smart contract based policies for the internet of things. Clust Comput 24:1–20. https://doi.org/10.1007/s10586-020-03216-w

    Article  Google Scholar 

  27. Zhang C, Zhu L, Xu C, Sharif K (2020) Prvb: achieving privacy-preserving and reliable vehicular crowdsensing via blockchain oracle. IEEE Trans Veh Technol 1–1. https://doi.org/10.1109/TVT.2020.3046027

  28. Laurent A, Laurent D, Madera C (2020). Data lakes. https://doi.org/10.1002/9781119720430

  29. Nilsback M-E (2006) Zisserman A. A visual vocabulary for flower classification 2:1447–1454. https://doi.org/10.1109/CVPR.2006.42

    Article  Google Scholar 

  30. Meier B, D’Agostino P, Elliot A, Maier M, Wilkowski B (2012) Color in context: psychological context moderates the influence of red on approach- and avoidance-motivated behavior. PloS one 7:40333. https://doi.org/10.1371/journal.pone.0040333

    Article  ADS  CAS  Google Scholar 

  31. Elliot A (2012) Maier M. Color-in-ontext Theory 45:61–125. https://doi.org/10.1016/B978-0-12-394286-9.00002-0

  32. Meregalli C, Canta A, Carozzi V, Chiorazzi A, Oggioni N, Gilardini A, Ceresa C, Avezza F, Crippa L, Marmiroli P, Cavaletti G (2009) Bortezomib-induced painful neuropathy in rats: a behavioral, neurophysiological and pathological study in rats. European Journal of Pain (London, England) 14:343–50. https://doi.org/10.1016/j.ejpain.2009.07.001

    Article  CAS  PubMed  Google Scholar 

  33. Smith J, Lin T, Ranson KJ (1980) The lambertian assumption and landsat data. Photogrammetric Engineering and Remote Sensing 46

Download references

Acknowledgements

This work was supported by the Key Research and Development Program of Shaanxi Province in 2023 (No.2023-YBGY-404, No. 2023-ZDLGY-48), the 2022 Research Project of Rural Public Cultural Service Research Institute, National Center for Public Culture Development, Ministry of Culture and Tourism (No.XCGGWH2022005), the 2022 Public Digital Cultural Service Project of National Center for Public Culture Development, Ministry of Culture and Tourism (No. GGSZWHFW2022-005), Shaanxi Province University Young Outstanding Talents Support Program, and State Environmental Protection Key Laboratory of Coastal Ecosystem (202110)

Author information

Authors and Affiliations

  1. Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi’an Polytechnic University, No.19 Jinhua South Road Xi’an, Xi’an, 710048, Shaanxi, China

    Xueqing Zhao, Yifan Feng & Xin Shi

  2. Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190, Beijing, China

    Yun Wang & Guigang Zhang

Authors
  1. Xueqing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guigang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xueqing Zhao.

Ethics declarations

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yifan Feng, Xin Shi, Yun Wang and Guigang Zhang contributed equally to this work.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Feng, Y., Shi, X. et al. A color constancy based flower classification method in the blockchain data lake. Multimed Tools Appl 83, 28657–28673 (2024). https://doi.org/10.1007/s11042-023-16656-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16656-4

Keywords

Search

Navigation