Worst-Separated Couple-Resolution Discriminant Analysis
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Low-resolution is an important issue when handling real world image recognition problems. The performance of traditional recognition algorithms, e.g. LDA/PCA, usually drops drastically due to the loss of discriminant information compared to those for high-resolution or super-resolution images. In order to solve this problem, many methods have been proposed in recent years based on coupled projections, i.e. learning two sets of different projections, one for high-resolution images and one for low-resolution images. For example, SDA (simultaneous discriminant analysis) obtains projections by maximizing the average between-class scatter while minimizing the average within-class scatters. Like LDA, SDA cannot separate projected classes, especially for those that are closer to each other. In this paper, a novel discriminant analysis method is proposed to achieve the optimal projections by maximizing the minimum distance between pair-wise classes. Experiments on several image datasets verify the efficiency of the presented methods.

    Reference
    Related
    Cited by
Get Citation

杨磊磊,陈松灿.最坏分离的联合分辨率判别分析.软件学报,2015,26(6):1386-1394

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:June 02,2013
  • Revised:March 27,2014
  • Adopted:
  • Online: June 04,2015
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063