Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector MachineReport as inadecuate




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School of Computer Science and Technology, Nanjing Normal University, 210023 Nanjing, China

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Department of Mathematics and Mechanics, China University of Mining and Technology, 221008 Xuzhou, China

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School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, 25443 West Virginia, WV, USA

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

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School of Psychology, Nanjing Normal University, 210008 Nanjing, China



Those authors contributed equally to this paper.





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Authors to whom correspondence should be addressed.



Academic Editor: Carlo Cattani

Abstract To develop an automatic tea-category identification system with a high recall rate, we proposed a computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures. We captured 300 tea images using a 3-CCD digital camera, and then extracted 64 color histogram features and 16 wavelet packet entropy WPE features to obtain color information and texture information, respectively. Principal component analysis was used to reduce features, which were fed into a fuzzy support vector machine FSVM. Winner-take-all WTA was introduced to help the classifier deal with this 3-class problem. The 10 × 10-fold stratified cross-validation results show that the proposed FSVM + WTA method yields an overall recall rate of 97.77%, higher than 5 existing methods. In addition, the number of reduced features is only five, less than or equal to existing methods. The proposed method is effective for tea identification. View Full-Text

Keywords: tea identification; wavelet packet entropy; Shannon entropy; wavelet analysis; support vector machine SVM; fuzzy SVM; information theory tea identification; wavelet packet entropy; Shannon entropy; wavelet analysis; support vector machine SVM; fuzzy SVM; information theory





Author: Shuihua Wang 1,4,†, Xiaojun Yang 2,†, Yudong Zhang 1,4,†,* , Preetha Phillips 3, Jianfei Yang 4 and Ti-Fei Yuan 5,*

Source: http://mdpi.com/



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