期刊
ENTROPY
卷 17, 期 10, 页码 6663-6682出版社
MDPI
DOI: 10.3390/e17106663
关键词
tea identification; wavelet packet entropy; Shannon entropy; wavelet analysis; support vector machine (SVM); fuzzy SVM; information theory
资金
- NSFC [61273243, 51407095]
- Natural Science Foundation of Jiangsu Province [BK20150982, BK20150983]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2013012-2, BE2014009-3]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [13KJB460011, 14KJB520021]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]
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 x 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.
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