Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy

Xingxing Zhou; Guangshuai Zhang; Zhengchao Dong; Shuihua Wang; Yudong Zhang

doi:10.1007/978-3-319-32557-6_5

Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy

Xingxing Zhou, Guangshuai Zhang, Zhengchao Dong, Shuihua Wang^*, Yudong Zhang

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers
Editors	Craig C. Douglas, Jiang Xie, Wu Zhang, Zhangxin Chen, Yan Chen, Yan Chen
Publisher	Springer Verlag
Pages	48-54
Number of pages	7
ISBN (Print)	9783319325569
DOIs	https://doi.org/10.1007/978-3-319-32557-6_5
Publication status	Published - 2016
Externally published	Yes
Event	3rd International Conference on High Performance Computing and Applications, HPCA 2015 - Shanghai, China Duration: 26 Jul 2015 → 30 Jul 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9576
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on High Performance Computing and Applications, HPCA 2015
Country/Territory	China
City	Shanghai
Period	26/07/15 → 30/07/15

Keywords

Feed-forward neural network
Tea classification
Two dimensional wavelet entropy

Access to Document

10.1007/978-3-319-32557-6_5

Cite this

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 C. C. Douglas, J. Xie, W. Zhang, Z. Chen, Y. Chen, & Y. Chen (Eds.), High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers (pp. 48-54). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9576). Springer Verlag. https://doi.org/10.1007/978-3-319-32557-6_5

Zhou, Xingxing ; Zhang, Guangshuai ; Dong, Zhengchao et al. / Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy. High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. editor / Craig C. Douglas ; Jiang Xie ; Wu Zhang ; Zhangxin Chen ; Yan Chen ; Yan Chen. Springer Verlag, 2016. pp. 48-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e587d83c1e2f43a0a70b115a0b9fff30,

title = "Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy",

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.",

keywords = "Feed-forward neural network, Tea classification, Two dimensional wavelet entropy",

author = "Xingxing Zhou and Guangshuai Zhang and Zhengchao Dong and Shuihua Wang and Yudong Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 3rd International Conference on High Performance Computing and Applications, HPCA 2015 ; Conference date: 26-07-2015 Through 30-07-2015",

year = "2016",

doi = "10.1007/978-3-319-32557-6_5",

language = "English",

isbn = "9783319325569",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "48--54",

editor = "Douglas, {Craig C.} and Jiang Xie and Wu Zhang and Zhangxin Chen and Yan Chen and Yan Chen",

booktitle = "High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers",

}

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 CC Douglas, J Xie, W Zhang, Z Chen, Y Chen & Y Chen (eds), High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9576, Springer Verlag, pp. 48-54, 3rd International Conference on High Performance Computing and Applications, HPCA 2015, Shanghai, China, 26/07/15. https://doi.org/10.1007/978-3-319-32557-6_5

Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy. / Zhou, Xingxing; Zhang, Guangshuai; Dong, Zhengchao et al.
High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. ed. / Craig C. Douglas; Jiang Xie; Wu Zhang; Zhangxin Chen; Yan Chen; Yan Chen. Springer Verlag, 2016. p. 48-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9576).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy

AU - Zhou, Xingxing

AU - Zhang, Guangshuai

AU - Dong, Zhengchao

AU - Wang, Shuihua

AU - Zhang, Yudong

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - (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.

AB - (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.

KW - Feed-forward neural network

KW - Tea classification

KW - Two dimensional wavelet entropy

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U2 - 10.1007/978-3-319-32557-6_5

DO - 10.1007/978-3-319-32557-6_5

M3 - Conference Proceeding

AN - SCOPUS:84979279886

SN - 9783319325569

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 48

EP - 54

BT - High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers

A2 - Douglas, Craig C.

A2 - Xie, Jiang

A2 - Zhang, Wu

A2 - Chen, Zhangxin

A2 - Chen, Yan

PB - Springer Verlag

T2 - 3rd International Conference on High Performance Computing and Applications, HPCA 2015

Y2 - 26 July 2015 through 30 July 2015

ER -

Zhou X, Zhang G, Dong Z, Wang S, Zhang Y. Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy. In Douglas CC, Xie J, Zhang W, Chen Z, Chen Y, Chen Y, editors, High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. Springer Verlag. 2016. p. 48-54. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-32557-6_5

Tea category classification based on feed-forward neural network and two-dimensional wavelet entropy

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Keywords

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