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

Document Type

Conference Proceeding

Publication Date

12-9-2015

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.

Identifier

84956622051 (Scopus)

ISBN

[9781479983391]

Publication Title

Proceedings International Conference on Image Processing Icip

External Full Text Location

https://doi.org/10.1109/ICIP.2015.7351113

ISSN

15224880

First Page

1810

Last Page

1814

Volume

2015-December

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