Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET-A, SET-B and SET-C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co-occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K-nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET-B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

中文翻译：

---

水稻籽粒纹理特征提取技术的比较分析

在这项研究中，基于各种质地模型，讨论了八种不同水稻粒的分类。针对分类任务，提出了四种局部纹理特征提取技术，并形成了三组纹理特征（SET-A，SET-B和SET-C）。将所提出的特征集的性能与基于游程长度矩阵，共现矩阵，大小区域矩阵，邻域灰度差矩阵和小波分解的现有技术进行比较，以利用反向传播神经网络（BPNN） ）。还针对来自Brodatz的纹理数据集的公开可用数据对提出的技术进行了测试，并将其结果与其他技术进行了比较。还将BPNN分类器的分类准确性与其他统计分类器（即K最近邻，线性判别分类器和朴素贝叶斯分类器）进行比较。发现，所提出的特征集在水稻数据和布罗达兹数据上均产生更好的分类结果。结果表明，特征SET-B可以对稻米进行分类，平均分类精度为99.63％，至少具有六个特征。