Tea category identification using computer vision and generalized eigenvalue proximal SVM

Shuihua Wang, Preetha Phillips, Aijun Liu, Sidan Du*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

(Objective) In order to increase classification accuracy of tea-category identification (TCI) system, this paper proposed a novel approach. (Method) The proposed methods first extracted 64 color histogram to obtain color information, and 16 wavelet packet entropy to obtain the texture information. With the aim of reducing the 80 features, principal component analysis was harnessed. The reduced features were used as input to generalized eigenvalue proximal support vector machine (GEPSVM). Winner-takes-all (WTA) was used to handle the multiclass problem. Two kernels were tested, linear kernel and Radial basis function (RBF) kernel. Ten repetitions of 10-fold stratified cross validation technique were used to estimate the out-of-sample errors. We named our method as GEPSVM + RBF + WTA and GEPSVM + WTA. (Result) The results showed that PCA reduced the 80 features to merely five with explaining 99.90% of total variance. The recall rate of GEPSVM + RBF + WTA achieved the highest overall recall rate of 97.9%. (Conclusion) This was higher than the result of GEPSVM + WTA and other five state-of-the-art algorithms: back propagation neural network, RBF support vector machine, genetic neural-network, linear discriminant analysis, and fitness-scaling chaotic artificial bee colony artificial neural network.

Original languageEnglish
Pages (from-to)325-339
Number of pages15
JournalFundamenta Informaticae
Volume151
Issue number1-4
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Artificial neural network
  • Color histogram
  • Computer vision
  • Pattern recognition
  • Radial basis function
  • Support vector machine
  • Tea category identification
  • Wavelet packet entropy
  • Winner-takes-all

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