基于支持向量机的珍珠多特征分类方法

Pearl multi-feature classification method based on support vector machine

珍珠企业在珍珠分类的过程中需要同时考虑珍珠的形状、纹理和色泽等特征信息,传统珍珠分类方法只针对单一特征对其进行分类,因此提取珍珠的多个特征对其进行分类有着现实意义.在利用单目多视角摄像装置直接获取5个不同视角的珍珠表面图像并进行预处理之后,参考实际人工分类的步骤,用珍珠边缘轮廓得到其傅里叶级数的系数作为形状特征,并用灰度共生矩阵得到珍珠的全局纹理特征,此外还设计了一种新的局部纹理特征提取方法;通过从珍珠的多个视图中提取珍珠的形状特征、全局纹理特征和局部纹理特征,进而构建支持向量机分类器,实现二分类.实验结果表明:所提出的特征提取方法有效,在1 100颗测试珍珠上分类精度达到85.73%.

Pearl companies need to consider the characteristics of pearls such as shape,texture,and color in the process of pearl classification.Traditional pearl classification methods only classify them by single feature,so extracting multiple features to classify pearls has important practical significance.The pearl’s surface images from five different visual angles were obtained by a monocular multi-view imaging device and preprocessed.Then,according to the actual manual classification steps,the Fourier series coefficients was computed as the shape features of the pearl and its global texture features was extracted based on the gray-level co-occurrence matrix.Moreover,as the complement of the global texture features,a novel method was developed to extract the local texture features.The SVM model was constructed by utilizing the proposed shape features,the global texture features,and the local texture features.The binary classification of pearls was realized.The experimental results showed that the presented method is quite efficient,and the classification accuracy arrives 85.73%on 1 100 pearls with properly selected kernel function.