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.

中文翻译：

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基于视觉特征融合的优化支持向量机评估哈密枣干的表面质地。

枣干果的表面质地是重要的质量分级标准。本文介绍了一种基于连接区域密度，纹理特征和颜色特征的新颖视觉特征融合。利用单尺度二维离散小波变换对干燥的哈密枣图像进行视觉特征提取前的单尺度分解和重构。连通区域密度通过两种不同的算法提取，而纹理特征通过灰度共生矩阵提取，颜色特征通过图像处理算法提取。基于对视觉特征进行相关分析得到的选定特征，优化后的支持向量机分类模型的准确率为96.67％。