An Image Classification Algorithm Integrating Principal Component Analysis and Spatial Pyramid Matching Features

被引：0
|
作者：
Li, Qingyan [1 ]
Peng, Jinye [2 ]
Li, Zhan [2 ]
Ren, Yifeng [3 ]
机构：
[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Shaanxi, Peoples R China
[2] Northwest Univ, Sch Informat & Technol, Xian 710127, Shaanxi, Peoples R China
[3] Xijing Univ, Sch Informat Engn, Xian 710127, Shaanxi, Peoples R China
来源：
2017 FOURTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP) | 2017年
基金：
中国博士后科学基金;
关键词：
Image classification; Principal Component Analysis; Bag-of-words; SPM; REDUCTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
To speed the image classification by effectively eliminating of redundancy in image features is a hot research topic in computer vision. Integrating the Spatial Pyramid Matching (SPM) and Principal Component Analysis (PCA), we propose a new SPM-PCA algorithm for effective image classification. Our method can improve time efficiency and meanwhile improve classification performance through dimensionality reduction in multiple levels of spatial pyramid Experiments demonstrate our method's performance.
引用
收藏
页码：557 / 562
页数：6
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