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

Qingfeng Liu; Ajit Puthenputhussery; Chengjun Liu

doi:10.1109/ICIP.2015.7351113

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 proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
Publisher	IEEE Computer Society
Pages	1810-1814
Number of pages	5
ISBN (Electronic)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7351113
State	Published - Dec 9 2015
Externally published	Yes
Event	IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: Sep 27 2015 → Sep 30 2015

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2015-December
ISSN (Print)	1522-4880

Other

Other	IEEE International Conference on Image Processing, ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	9/27/15 → 9/30/15

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2015.7351113

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Liu, Q., Puthenputhussery, A., & Liu, C. (2015). Novel general KNN classifier and general nearest mean classifier for visual classification. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 1810-1814). Article 7351113 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351113

@inproceedings{fe76073447604e94ba2894a837659b4e,

title = "Novel general KNN classifier and general nearest mean classifier for visual classification",

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.",

author = "Qingfeng Liu and Ajit Puthenputhussery and Chengjun Liu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

year = "2015",

month = dec,

day = "9",

doi = "10.1109/ICIP.2015.7351113",

language = "English (US)",

series = "Proceedings - International Conference on Image Processing, ICIP",

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booktitle = "2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings",

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}

Liu, Q, Puthenputhussery, A & Liu, C 2015, Novel general KNN classifier and general nearest mean classifier for visual classification. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings., 7351113, Proceedings - International Conference on Image Processing, ICIP, vol. 2015-December, IEEE Computer Society, pp. 1810-1814, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 9/27/15. https://doi.org/10.1109/ICIP.2015.7351113

Novel general KNN classifier and general nearest mean classifier for visual classification. / Liu, Qingfeng; Puthenputhussery, Ajit; Liu, Chengjun.
2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. p. 1810-1814 7351113 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Novel general KNN classifier and general nearest mean classifier for visual classification

AU - Liu, Qingfeng

AU - Puthenputhussery, Ajit

AU - Liu, Chengjun

PY - 2015/12/9

Y1 - 2015/12/9

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings - International Conference on Image Processing, ICIP

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EP - 1814

BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings

PB - IEEE Computer Society

T2 - IEEE International Conference on Image Processing, ICIP 2015

Y2 - 27 September 2015 through 30 September 2015

ER -

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

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