Non-parametric classification algorithm with an unknown class

B.G.H. Gorte; N. Gorte-Kroupnova

doi:10.1109/ISCV.1995.477042

Non-parametric classification algorithm with an unknown class

B.G.H. Gorte
, N. Gorte-Kroupnova

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

86 Downloads (Pure)

Abstract

In the classification of pixels of a multispectral image by methods of supervised classification, a problem can arise in case when an unknown class is present. In this paper, we suggest a method that gives good results in such a case. The method provides an estimation for a posteriori probability vectors (and consequently, classification), and, besides, estimates the prior probability of classes, including the unknown one, and thus, the areas occupied by every class.

Original language	English
Title of host publication	Proceedings of International Symposium on Computer Vision - ISCV 1995
Place of Publication	Los Alamitos, CA
Publisher	IEEE
Pages	443-448
Number of pages	6
ISBN (Print)	0-8186-7190-4
DOIs	https://doi.org/10.1109/ISCV.1995.477042
Publication status	Published - 1995
Event	IEEE International Symposium on Computer Vision, ICCV 1995 - Coral Gables, United States Duration: 21 Nov 1995 → 23 Nov 1995

Conference

Conference	IEEE International Symposium on Computer Vision, ICCV 1995
Abbreviated title	ICCV
Country/Territory	United States
City	Coral Gables
Period	21/11/95 → 23/11/95

Keywords

ADLIB-ART-568
EOS

Access to Document

10.1109/ISCV.1995.477042

ArticleFinal published version, 777 KB

Cite this

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title = "Non-parametric classification algorithm with an unknown class",

abstract = "In the classification of pixels of a multispectral image by methods of supervised classification, a problem can arise in case when an unknown class is present. In this paper, we suggest a method that gives good results in such a case. The method provides an estimation for a posteriori probability vectors (and consequently, classification), and, besides, estimates the prior probability of classes, including the unknown one, and thus, the areas occupied by every class.",

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AU - Gorte-Kroupnova, N.

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AB - In the classification of pixels of a multispectral image by methods of supervised classification, a problem can arise in case when an unknown class is present. In this paper, we suggest a method that gives good results in such a case. The method provides an estimation for a posteriori probability vectors (and consequently, classification), and, besides, estimates the prior probability of classes, including the unknown one, and thus, the areas occupied by every class.

KW - ADLIB-ART-568

KW - EOS

U2 - 10.1109/ISCV.1995.477042

DO - 10.1109/ISCV.1995.477042

M3 - Conference contribution

SN - 0-8186-7190-4

SP - 443

EP - 448

BT - Proceedings of International Symposium on Computer Vision - ISCV 1995

PB - IEEE

CY - Los Alamitos, CA

T2 - IEEE International Symposium on Computer Vision, ICCV 1995

Y2 - 21 November 1995 through 23 November 1995

ER -

Non-parametric classification algorithm with an unknown class

Abstract

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Keywords

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