摘要
针对传统的岩石薄片成分分析耗时、识别率不高等问题,提出了一种基于协同表示(CR)的岩石薄片成分分析方法。首先,分析探讨了岩石薄片中颗粒纹理特性,证明将薄片图像的分层多尺度局部二值化(HMLBP)特征与灰度共生矩阵(GLCM)特征相融合能有效地表征岩石薄片中颗粒的纹理。然后,为降低识别阶段时间复杂度,采用主成分分析(PCA)方法将新特征降维到100维。最后,采用基于协同表示的分类器(CRC)进行分类识别。与基于稀疏表示的分类器(SRC)分别采用样本字典中某一个样本单独编码表征预测样本不同,基于协同表示的分类器采用样本字典中的所有样本协同编码表征预测样本,借助不同样本的同一属性提高识别率。实验结果表明该方法的识别速度较基于稀疏的分类器识别方法提高300%,识别率提高2%;在实践应用中能较好地区分岩石薄片中的石英成分和长石成分。
To solve the issues of time-consuming and low recognition rate in the traditional component analysis of rock slices,a method of component analysis of rock slices based on Collaborative Representation（ CR） was proposed. Firstly,texture feature of grain in rock slices was discussed,and the way of combining Hierarchical Multi-scale Local Binary Pattern（ HMLBP） and Gray Level Co-occurrence Matrix（ GLCM） was proved to characterize the texture of grain in rock slices well.Then,in order to reduce the time complexity of classification,the dimension of new features was reduced to 100 by using Principal Component Analysis（ PCA）. Finally,the Collaborative Representation based Classification（ CRC） was used as the classifier. Differ to Sparse Representation based Classification（ SRC）,prediction samples were encoded by all the samples in train dictionary collaboratively instead of some single sample alone. Same attributes of different samples can improve the recognition rate. The experimental results show that the recognition speed of the method increases by 300% and the recognition rate of the method increases by 2% compared to SRC. In practical application,it can distinguish quartz and feldspar components in rock slices well.
出处
《计算机应用》
CSCD
北大核心
2016年第3期854-858,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61372174)~~
关键词
协同表示
纹理特征
特征融合
分类器
岩石薄片
Collaborative Representation（CR）
Texture Feature（TF）
Feature Fusion（FF）
classify
Rock Slice（RS）
