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Entropy 2016, 18(3), 77; doi:10.3390/e18030077

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

1
School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
2
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China
3
Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, Nanning 530021, China
4
Department of Mathematics and Mechanics, China University of Mining and Technology, Xuzhou 221008, China
5
Engineering School (DEIM), University of Tuscia, Viterbo 01100, Italy
6
Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395007, India
7
School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 14 October 2015 / Revised: 15 January 2016 / Accepted: 16 February 2016 / Published: 27 February 2016
(This article belongs to the Special Issue Computational Complexity)
View Full-Text   |   Download PDF [3246 KB, uploaded 27 February 2016]   |  

Abstract

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification. View Full-Text
Keywords: tea-category identification; fractional Fourier entropy; color histogram; kernel principal component analysis; feed-forward neural network; Jaya algorithm; stratified cross validation tea-category identification; fractional Fourier entropy; color histogram; kernel principal component analysis; feed-forward neural network; Jaya algorithm; stratified cross validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, Y.; Yang, X.; Cattani, C.; Rao, R.V.; Wang, S.; Phillips, P. Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm. Entropy 2016, 18, 77.

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