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.
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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).
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We have no conflicts of interest to disclose with regard to the subject matter of this paper.
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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
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DOI: https://doi.org/10.1007/978-3-319-32557-6_5
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