Local information enhanced LBP  

作　　者：张刚 苏光大 陈健生 王晶 

机构地区：[1]Department of Electronic Engineering,Tsinghua University [2]School of Software,Shenyang University of Technology

出　　处：《Journal of Central South University》2013年第11期3150-3155,共6页中南大学学报（英文版）

基　　金：Project(61372176,51109112)supported by the National Natural Science Foundation of China;Project(2012M520277)supported by theChina Postdoctoral Science Foundation

摘　　要：Based on the observation that there exists multiple information in a pixel neighbor,such as distance sum and gray difference sum,local information enhanced LBP(local binary pattern)approach,i.e.LE-LBP,is presented.Geometric information of the pixel neighborhood is used to compute minimum distance sum.Gray variation information is used to compute gray difference sum.Then,both the minimum distance sum and the gray difference sum are used to build a feature space.Feature spectrum of the image is computed on the feature space.Histogram computed from the feature spectrum is used to characterize the image.Compared with LBP,rotation invariant LBP,uniform LBP and LBP with local contrast,it is found that the feature spectrum image from LE-LBP contains more details,however,the feature vector is more discriminative.The retrieval precision of the system using LE-LBP is91.8%when recall is 10%for bus images.Based on the observation that there exists multiple information in a pixel neighbor, such as distance sum and gray difference sum, local information enhanced LBP （local binary pattern） approach, i.e. LE-LBP, is presented. Geometric information of the pixel neighborhood is used to compute minimum distance sum. Gray variation information is used to compute gray difference sum. Then, both the minimum distance sum and the gray difference sum are used to build a feature space. Feature spectrum of the image is computed on the feature space. Histogram computed from the feature spectrum is used to characterize the image. Compared with LBP, rotation invariant LBP, uniform LBP and LBP with local contrast, it is found that the feature spectrum image from LE-LBP contains more details, however, the feature vector is more discriminative. The retrieval precision of the system using LE-LBP is 91.8% when recall is 10% for bus images.

关 键 词：texture feature extraction LE-LBP minimum distance sum gray difference sum 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] Q513.01[自动化与计算机技术—计算机科学与技术]