Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm
AbstractThis 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
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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.
Zhang Y, Yang X, Cattani C, Rao RV, Wang S, Phillips P. Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm. Entropy. 2016; 18(3):77.Chicago/Turabian Style
Zhang, Yudong; Yang, Xiaojun; Cattani, Carlo; Rao, Ravipudi V.; Wang, Shuihua; Phillips, Preetha. 2016. "Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm." Entropy 18, no. 3: 77.
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