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Food Science and Biotechnology
→ Food Science and Biotechnology 2020 ; 29(4): 493-502
Evaluation of surface texture of dried Hami Jujube using optimized support vector machine based on visual features fusion
Xiuzhi Luo1 • Benxue Ma1,2,3 • Wenxia Wang1 • Shengyuan Lei1 • Yangyang Hu1 • Guowei Yu1,2,3 • Xiaozhan Li1,2,3
1 College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China 2 Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi, P.R. China 3 Shihezi University, Shihezi 832003, Xinjiang, China
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 singlescale 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.
KEYWORD
Features extraction · Correlation analysis · Dried Hami jujube · Optimized SVM · Visual features fusion
Food Science and Biotechnology 2020 ; 29(4): 493-502
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