测控技术2017，Vol.36Issue(3)：20-23,27,5.
一种低冗余Dense SIFT特征提取方法
A Low-Redundancy Dense SIFT Feature Extraction Algorithm
摘要
Abstract
Feature extraction is one of the key parts in image classification.The existing Dense SIFT feature method adopts fixed grid and step-size to extract features by scanning way from top to bottom and left to right.If image resolution is too high,more image features will be extracted,so that a lot of redundancy information will be introduced.Therefore,a low-redundancy Dense SIFT feature extraction algorithm is proposed.In this al gorithm,the preprocessing is executed on the image,which can produce the compact expression of image.Then,the centralization idea and the e0 norm are exploited to optimize Dense SIFT features for removing the re dundant feature points,in order to finally improve the description ability of features.Finally,the low-redundancy Dense SIFT is applied to image classification.Experimental results show that the proposed scheme can reduce the number of feature descriptors and improve the performance of feature.关键词
图像分类/稀疏表示/特征提取/图像预处理Key words
image classification/sparse representation/feature extraction/image preprocessing分类
信息技术与安全科学引用本文复制引用
龙海霞,卓力,李嘉锋,张菁..一种低冗余Dense SIFT特征提取方法[J].测控技术,2017,36(3):20-23,27,5.基金项目
国家自然科学基金项目(61372149) （61372149）
