A new locally linear KNN method with an improved marginal Fisher analysis for image classification

Qingfeng Liu, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This paper presents a novel locally linear KNN method with an improved marginal Fisher analysis for image classification. First, the discriminating color space (DCS), which is derived by discriminant analysis of the red, green, and blue primary colors, is integrated into the proposed method. Second, an improved marginal Fisher analysis (IMFA) applies an eigenvalue spectrum analysis to improve the generalization performance of the marginal Fisher analysis method. Third, a new locally linear KNN classifier (LLKNN), which represents the test image as a linear combination of its k nearest training images and assigns it to the class with the largest sum of weights, is presented to improve upon the traditional KNN approach. The effectiveness of the proposed method is evaluated on two representative datasets, namely the AR face image data set and the ETH-80 image data set. Experimental results show that the proposed method performs better than some representative state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationIJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479935840
DOIs
StatePublished - Dec 23 2014
Event2nd IEEE/IAPR International Joint Conference on Biometrics, IJCB 2014 - Clearwater, United States
Duration: Sep 29 2014Oct 2 2014

Publication series

NameIJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics

Other

Other2nd IEEE/IAPR International Joint Conference on Biometrics, IJCB 2014
CountryUnited States
CityClearwater
Period9/29/1410/2/14

All Science Journal Classification (ASJC) codes

  • Biotechnology

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