Luo Xiuzhi, Ma Benxue, Wang Wenxia, Lei Shengyuan, Hu Yangyang, Yu Guowei, Li Xiaozhan
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003 Xinjiang China.
2Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi, P.R. China.
Food Sci Biotechnol. 2019 Nov 27;29(4):493-502. doi: 10.1007/s10068-019-00683-9. eCollection 2020 Apr.
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.
干枣果实的表面纹理是一个重要的质量分级标准。本文介绍了一种基于连通区域密度、纹理特征和颜色特征的新型视觉特征融合方法。在提取视觉特征之前，使用单尺度二维离散小波变换对干哈密枣图像进行单尺度分解和重构。通过两种不同的算法提取连通区域密度，利用灰度共生矩阵提取纹理特征，采用图像处理算法提取颜色特征。基于通过视觉特征相关性分析获得的选定特征，优化后的支持向量机分类模型的准确率为96.67%。与极限学习机分类模型和其他融合方法相比，基于选定视觉特征融合的优化支持向量机表现更优。
