Entropy (Feb 2016)

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

  • Yudong Zhang,
  • Xiaojun Yang,
  • Carlo Cattani,
  • Ravipudi Venkata Rao,
  • Shuihua Wang,
  • Preetha Phillips

DOI
https://doi.org/10.3390/e18030077
Journal volume & issue
Vol. 18, no. 3
p. 77

Abstract

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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.

Keywords