基于协同表示的多特征融合岩石分类

doi:10.11772/j.issn.1001-9081.2016.03.854

计算机应用 ›› 2016, Vol. 36 ›› Issue (3): 854-858.DOI: 10.11772/j.issn.1001-9081.2016.03.854

基于协同表示的多特征融合岩石分类

刘珏先, 滕奇志, 王正勇, 何小海

四川大学电子信息学院, 成都 610061

收稿日期:2015-08-18 修回日期:2015-10-10 出版日期:2016-03-10 发布日期:2016-03-17
通讯作者: 滕奇志
作者简介:刘珏先(1987-),男,广西南宁人,硕士研究生,主要研究方向:图像处理与模式识别;滕奇志(1962-),女,四川成都人,教授,博士生导师,博士,主要研究方向:图像处理与模式识别、三维重建;王正勇(1969-),女,四川达州人,副教授,博士,主要研究方向:图像处理与模式识别、多媒体通信;何小海(1964-),男,四川成都人,教授,博士生导师,博士,主要研究方向:图像处理与模式识别、网络通信。
基金资助:
国家自然科学基金资助项目(61372174)。

Rock classification of multi-feature fusion based on collaborative representation

LIU Juexian, TENG Qizhi, WANG Zhengyong, HE Xiaohai

College of Electronics and information Engineering, Sichuan university, Chengdu Sichuan 610061, China

Received:2015-08-18 Revised:2015-10-10 Online:2016-03-10 Published:2016-03-17
Supported by:
This work is supported by the National Natural Science Foundation of China (61372174).

摘要/Abstract

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。

关键词: 协同表示, 纹理特征, 特征融合, 分类器, 岩石薄片

Abstract: 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.

Key words: Collaborative Representation (CR), Texture Feature (TF), Feature Fusion (FF), classify, Rock Slice (RS)

中图分类号:

TP391.41

刘珏先, 滕奇志, 王正勇, 何小海. 基于协同表示的多特征融合岩石分类[J]. 计算机应用, 2016, 36(3): 854-858.

LIU Juexian, TENG Qizhi, WANG Zhengyong, HE Xiaohai. Rock classification of multi-feature fusion based on collaborative representation[J]. Journal of Computer Applications, 2016, 36(3): 854-858.

参考文献

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基于协同表示的多特征融合岩石分类

Rock classification of multi-feature fusion based on collaborative representation

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