现代电子技术2017，Vol.40Issue(10)：95-98,102,5.DOI:10.16652/j.issn.1004-373x.2017.10.026
基于p.d.f特征的分层稀疏表示在图像分类中的应用
Application of hierarchical sparse representation based on p.d.f feature in image classification
摘要
Abstract
In order to construct the meaningful image representation in computer vision task,a novel hierarchical sparse representation method based on oriented histogram feature of probability density function(p.d.f)gradients is proposed for image classification. The traditional hierarchical sparse representation method which learns the image representation with SIFT descrip-tor or learn it directly from image block has poor discrimination. A universal p.d.f feature is employed for hierarchical learning, and the spatial pyramid max pooling method is used to construct the image-level sparse representation. The experimental results show that the algorithm has robustness and availability,and the classification accuracy for classifying the datasets of UIUC-Sports,Oxford Flowers and Scene 15 can reach up to 87.3%,86.6% and 84.1% respectively.关键词
图像分类/分层稀疏表示/空间金字塔最大池化/图像表示Key words
image classification/hierarchical sparse representation/spatial pyramid max pooling/image representation分类
信息技术与安全科学引用本文复制引用
王博..基于p.d.f特征的分层稀疏表示在图像分类中的应用[J].现代电子技术,2017,40(10):95-98,102,5.基金项目
国家重点基础研究发展计划(国家"973")项目:网络大数据感知融合与表示方法研究(2014CB340403) （国家"973"）
