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Evaluation of surface texture of dried Hami Jujube using optimized support vector machine based on visual features fusion

Food Science and Biotechnology volume 29, pages 493–502 (2020)Cite this article

Abstract

The surface texture of dried jujube fruits is a significant quality grading criterion. This paper introduced a novel visual feature fusion based on connected region density, texture features, and color features. The single-scale Two-Dimensional Discrete Wavelet Transform was used to perform single-scale decomposition and reconstruction of dried Hami jujube image before visual features extraction. The connected region density was extracted by the two different algorithms, whereas the texture features were extracted by Gray Level Co-occurrence Matrix and the color features were extracted by image processing algorithms. Based on selected features which obtained by correlation analysis of visual features, the accuracy rate of the optimized Support Vector Machine classification model was 96.67%. In comparing with Extreme Learning Machine classification model and other fusion methods, the optimized Support Vector Machine based on selected visual features fusion was better.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Grant Number 61763043) and the National Key Technologies R&D Program of China (Grant Numbers 2015BAD19B03).

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Affiliations

  1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, Xinjiang, China

    Xiuzhi Luo, Benxue Ma, Wenxia Wang, Shengyuan Lei, Yangyang Hu, Guowei Yu & Xiaozhan Li

  2. Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi, P.R. China

    Benxue Ma, Guowei Yu & Xiaozhan Li

  3. Shihezi University, Shihezi, 832003, Xinjiang, China

    Benxue Ma, Guowei Yu & Xiaozhan Li

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  1. Xiuzhi Luo
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  2. Benxue Ma
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  3. Wenxia Wang
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  4. Shengyuan Lei
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  5. Yangyang Hu
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  6. Guowei Yu
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  7. Xiaozhan Li
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Correspondence to Benxue Ma.

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Luo, X., Ma, B., Wang, W. et al. Evaluation of surface texture of dried Hami Jujube using optimized support vector machine based on visual features fusion. Food Sci Biotechnol 29, 493–502 (2020). https://doi.org/10.1007/s10068-019-00683-9

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  • DOI: https://doi.org/10.1007/s10068-019-00683-9

Keywords

  • Features extraction
  • Correlation analysis
  • Dried Hami jujube
  • Optimized SVM
  • Visual features fusion