基于局部排序差值细化模式的纹理图像分类  

Texture Image Classification Based on Local Sorted Difference Refinement Pattern

作　　者：束鑫[1,2] 潘慧 邵长斌 史金龙[1] 吴小俊[3] Shu Xin;Pan Hui;Shao Changbin;Shi Jinlong;Wu Xiaojun(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003;Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(Nanjing University of Science and Technology),Nanjing 210094;School of IOT Engineering,Jiangnan University,Wuxi 214122)

机构地区：[1]江苏科技大学计算机学院,镇江212003 [2]高维信息智能感知与系统教育部重点实验(南京理工大学),南京210094 [3]江南大学物联网工程学院,无锡214122

出　　处：《计算机辅助设计与图形学学报》2020年第12期1948-1956,1966,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家重点研发计划(2018YFC0309100,2018YFC0309104);国家自然科学基金面上项目(61672265,61772244,61876072);高维信息智能感知与系统教育部重点实验室创新基金(JYB201711).

摘　　要：针对传统局部二值模式及其扩展算法存在特征维度高、不能充分体现局部邻域像素间差值大小信息等问题,提出一种局部排序差值细化模式(LSDRP).首先根据采样半径大小对图像进行相应规格的高斯滤波,并按灰度值将局部邻域采样点排序;然后计算局部排序邻域内像素间的差值并将其融入排序二值编码对应位置的权值中,从而生成LSDRP特征;最后选取LSDRP特征模式中的高频模式表征图像,并级联多个半径下LSDRP特征的高频模式构成图像纹理的多尺度表示.在Outex,CUReT和UMD纹理库上的实验结果表明,所提算法计算简单且能在低维度条件下有效解决纹理分类中存在的光照、旋转变化问题;特别是在TC10,TC12_000和TC12_001纹理库上仅需120维特征即可分别达到100%,99.38%和99.72%的分类精度.The traditional local binary pattern and its variants have some shortcomings,such as high feature dimensions,not fully considering the difference between neighboring pixels in local areas and etc.We propose a novel local sorted difference refinement pattern(LSDRP)to overcome the above mentioned deficiencies.Firstly,the texture image is filtered with different Gaussian filters according to the sampling radius,and then the local neighborhood sampling points are sorted according to the pixel value.Secondly,the difference between neighboring pixels in the local area is integrated into the corresponding weight of the sorted binary code for the LSDRP feature generation.Finally,the high-frequency patterns in the LSDRP are selected to represent the texture image and the multi-scale LSDRP feature vector are cascaded to describe the image texture.The experimental results on Outex,CUReT and UMD texture datasets show that the proposed method is simple to calculate and robust to illumination and rotation variant with low-dimensional features。It is worth noting that the classification accuracies are 100%,99.38%,and 99.72%on the TC10,TC12_000,and TC12_001 texture datasets,respectively。

关 键 词：纹理分类 局部二值模式 排序二值模式 差值细化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]