基于稀疏表示的烤烟烟叶品质分级研究

Grading for Tobacco Leaf Quality Based on Sparse Representation

摘要:

为了实现烟叶自动检测与分析，通过计算机视觉对烟叶品质进行分级。在提取烟叶图像特征参数的基础上，提出了一种基于稀疏表示的烤烟烟叶品质分级方法。以临朐12种和恩施5种不同级别的烟叶图像作为研究对象，每级烟叶取10幅图像作为训练样本，对每幅烟叶图像取颜色、形态和纹理特征值。利用训练样本的特征值组成稀疏表示方法的数据字典，对每个测试样本计算其在数据字典上的投影，利用最小残差项确定其品质分级。实验结果与基追踪法（BP）、神经网络方法、SVM方法和模糊处理方法实验结果相比较，训练集样本识别率为100%，综合识别率达95.7%，取得了比较好的分类效果。

Abstract:

A quality grading method based on sparse representation was proposed to identify the varieties of tobacco quality. The images of 17 different qualities of tobacco were taken as objects. Ten images of each variety were selected randomly as training samples. The colors, morphological and textural characters of these images were extracted for making up the dictionary of sparse representation. The projection of the test image on the dictionary was calculated. The minimum projection error was regarded as the certain kind of tobacco. The result of the proposed method was compared with basic pursuit algorithm, neural network, SVM and fuzzy processing. The identification accuracy of training samples was 100% and the overall one was 95.7%.