Skip to main content
SpringerLink
Log in
Book cover

International Conference on High Performance Computing and Applications

HPCA 2015: High Performance Computing and Applications pp 48–54Cite as

Tea Category Classification Based on Feed-Forward Neural Network and Two-Dimensional Wavelet Entropy

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9576))

Abstract

(Aim) Tea plays a significant role because of its high value throughout the world. Computer vision techniques were successfully employed for rapid identification of teas. (Method) In our work, we present a computer assisted discrimination system on the basis of two steps: (i) two-dimensional wavelet-entropy for feature extraction; (ii) the feedforward Neural Network (FNN) for classification. Specifically, the wavelet entropy features were fed into a FNN classifier. (Results) The 10 runs of 75 images of three categories showed that the average accuracy achieved 90.70 %. The sensitivities of green, Oolong, and black tea are 92.80 %, 84.60 %, and 96.30 %, respectively. (Conclusions) It was easily observed that the proposed classifier can distinguish tea categories with satisfying performances, which was competitive with recent existing systems.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Choi, S.J., Park, S.Y., Park, J.S., Park, S.K., Jung, M.Y.: Contents and compositions of policosanols in green tea (Camellia sinensis) leaves. Food Chem. 204, 94–101 (2016)

    Article  Google Scholar 

  2. Diniz, P., Barbosa, M.F., Milanez, K., Pistonesi, M.F., de Araujo, M.C.U.: Using UV-V is spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup. Food Chem. 192, 374–379 (2016)

    Article  Google Scholar 

  3. Yuvaraj, D., Hariharan, S.: Content-based image retrieval based on integrating region segmentation and colour histogram. Int. Arab J. Inf. Technol. 13, 203–207 (2016)

    Google Scholar 

  4. Shahverdi, R., Tavana, M., Ebrahimnejad, A., Zahedi, K., Omranpour, H.: An improved method for edge detection and image segmentation using fuzzy cellular automata. Cybern. Syst. 47, 161–179 (2016)

    Article  Google Scholar 

  5. Zhou, H.L., Llewellyn, L., Wei, L., Creighton, D., Nahavandi, S.: Marine object detection using background modelling and blob analysis. In: 2015 IEEE International Conference on Systems, Man and Cybernetics, pp. 430–435. IEEE Computer Society, Los Alamitos (2015)

    Google Scholar 

  6. Anada, K., Kikuchi, T., Koka, S., Miyadera, Y., Yaku, T.: A method of ridge detection in triangular dissections generated by homogeneous rectangular dissections. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015, vol. 612, pp. 131–142. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  7. Sun, P., Wang, S., Phillips, P., Zhang, Y.: Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med. Mater. Eng. 26, 1283–1290 (2015)

    Article  Google Scholar 

  8. Korkmaz, S.A.: Diagnosis of cervical cancer cell taken from scanning electron and atomic force microscope images of the same patients using discrete wavelet entropy energy and Jensen Shannon, Hellinger, Triangle Measure classifier. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 160, 39–49 (2016)

    Article  Google Scholar 

  9. Hu, K.T., Liu, Z.G., Lin, S.S.: Wavelet entropy-based traction inverter open switch fault diagnosis in high-speed railways. Entropy 18, 19 (2016)

    Article  Google Scholar 

  10. Zhou, X.X., Zhang, Y.D., Ji, G.L., Yang, J.Q., Dong, Z.C., Wang, S.H., Zhang, G.S., Phillips, P.: Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Trans. Electr. Electron. Eng. 11, 364–373 (2016)

    Article  Google Scholar 

  11. Wang, S., Dong, Z., Du, S., Ji, G., Yan, J., Yang, J., Wang, Q., Feng, C., Phillips, P.: Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int. J. Imaging Syst. Technol. 25, 153–164 (2015)

    Article  Google Scholar 

  12. Wang, S., Yang, X., Zhang, Y., Phillips, P., Yang, J., Yuan, T.-F.: Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17, 6663–6682 (2015)

    Article  Google Scholar 

  13. Chandar, S.K., Sumathi, M., Sivanadam, S.N.: Forecasting gold prices based on extreme learning machine. Int. J. Comput. Commun. Control 11, 372–380 (2016)

    Article  Google Scholar 

  14. Zhou, X., Wang, S., Xu, W., Ji, G., Phillips, P., Sun, P., Zhang, Y.: Detection of pathological brain in MRI scanning based on wavelet-entropy and Naive Bayes classifier. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 201–209. Springer, Heidelberg (2015)

    Google Scholar 

  15. Turnbull, O., Lawry, J., Lowenberg, M., Richards, A.: A cloned linguistic decision tree controller for real-time path planning in hostile environments. Fuzzy Sets Syst. 293, 1–29 (2016)

    Article  MathSciNet  Google Scholar 

  16. Zhang, Y., Wang, S., Dong, Z., Phillips, P., Ji, G., Yang, J.: Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog. Electromagnet. Res. 152, 41–58 (2015)

    Article  Google Scholar 

  17. Schumann, A., Kralisch, C., Bar, K.-J.: Spectral decomposition of pupillary unrest using wavelet entropy. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, pp. 6154–6157 (2015)

    Google Scholar 

  18. Langley, P.: Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation. Entropy 17, 6397–6411 (2015)

    Article  Google Scholar 

  19. Ji, G., Yang, J., Wu, J., Wei, L.: Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17, 5711–5728 (2015)

    Article  Google Scholar 

  20. Lahmiri, S.: Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques. Phys. A 444, 388–396 (2016)

    Article  MathSciNet  Google Scholar 

  21. Asadi, R., Asadi, M., Kareem, S.A.: An efficient semisupervised feedforward neural network clustering. AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf. 30, 1–15 (2016)

    Article  Google Scholar 

  22. Zhang, Y., Wu, L.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst. Appl. 36, 8849–8854 (2009)

    Article  Google Scholar 

  23. Doh, J., Lee, S.U., Lee, J.: Back-propagation neural network-based approximate analysis of true stress-strain behaviors of high-strength metallic material. J. Mech. Sci. Technol. 30, 1233–1241 (2016)

    Article  Google Scholar 

  24. Khan, Y.: Partial discharge pattern analysis using PCA and back-propagation artificial neural network for the estimation of size and position of metallic particle adhering to spacer in GIS. Electr. Eng. 98, 29–42 (2016)

    Article  Google Scholar 

  25. Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Prob. Eng. 2015, 38 (2015)

    MathSciNet  Google Scholar 

  26. Zhang, Y., Phillips, P., Wang, S., Ji, G., Yang, J., Wu, J.: Fruit classification by biogeography-based optimization and feedforward neural network. Expert Syst. 33(3), 239–253 (2016)

    Article  Google Scholar 

  27. Lu, S., Wang, S., Zhang, Y.: A note on the weight of inverse complexity in improved hybrid genetic algorithm. J. Med. Syst. 40, 1–2 (2016)

    Article  Google Scholar 

  28. Zhang, Y., Wu, L., Wang, S.: Solving two-dimensional HP model by firefly algorithm and simplified energy function. Math. Prob. Eng. 13, 1–9 (2013)

    MATH  Google Scholar 

Download references

Acknowledgment

This paper was supported by NSFC (61503188), Science Research Foundation of Hunan Provincial Education Department (12B023), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Natural Science Foundation of Jiangsu Province (BK20150983), Open Fund of Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, (SDL201608), Open Fund of Key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University (93K172016K17), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), Ordinary University Graduate Student Scientific Research Innovation Projects of Jiangsu Province (KYLX15_0768).

Conflict of Interest.

We have no conflicts of interest to disclose with regard to the subject matter of this paper.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, 1 Wenyuan, Nanjing, 210023, Jiangsu, China

    Xingxing Zhou, Shuihua Wang & Yudong Zhang

  2. Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau, Chengdu, 610225, Sichuan, China

    Xingxing Zhou

  3. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, 210042, Jiangsu, China

    Xingxing Zhou & Guangshuai Zhang

  4. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China

    Guangshuai Zhang

  5. Translational Imaging Division and MRI Unit, New York State Psychiatric Institute, Columbia University, New York, New York, 10032, USA

    Zhengchao Dong

  6. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, Jiangsu, China

    Shuihua Wang

Authors
  1. Xingxing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangshuai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengchao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuihua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yudong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shuihua Wang or Yudong Zhang .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Shanghai University, Shanghai, China

    Jiang Xie

  2. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxin Chen

  3. Mathematics Department, University of Wyoming, Laramie, Wyoming, USA

    Craig C. Douglas

  4. School of Computer Engineering, Shanghai University, Shanghai, China

    Wu Zhang

  5. Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Yan Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhou, X., Zhang, G., Dong, Z., Wang, S., Zhang, Y. (2016). Tea Category Classification Based on Feed-Forward Neural Network and Two-Dimensional Wavelet Entropy. In: Xie, J., Chen, Z., Douglas, C., Zhang, W., Chen, Y. (eds) High Performance Computing and Applications. HPCA 2015. Lecture Notes in Computer Science(), vol 9576. Springer, Cham. https://doi.org/10.1007/978-3-319-32557-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32557-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32556-9

  • Online ISBN: 978-3-319-32557-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation