Novel general KNN classifier and general nearest mean classifier for visual classification

Qingfeng Liu, Ajit Puthenputhussery, Chengjun Liu

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

3 Scopus citations

Abstract

This paper presents a novel general k nearest neighbour classifier (GKNNc) and a novel general nearest mean classifier (GNMc) for visual classification. Instead of treating the data equally, both GKNNc and GNMc assign a weight coefficient to each data. To achieve good performance, the conditions and properties of the weight coefficients for GKNNc and GNMc are further analysed. Then a sparse representation based method is proposed to derive the weight coefficients for both GKNNc and GNMc. Experimental results on several representative data sets, such as the Caltech 101 dataset and the MIT-67 indoor scenes dataset demonstrate the feasibility of the proposed methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages1810-1814
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period9/27/159/30/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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