Image thresholding based on random spatial sampling and majority voting

Yi HONG; Hanli WANG; Sam KWONG

doi:10.1109/ICMLC.2010.5580571

Image thresholding based on random spatial sampling and majority voting

Yi HONG, Hanli WANG, Sam KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

This paper presents a novel image thresholding algorithm, namely Random spatial sampling and Majority voting based Image Thresholding (RMIT) algorithm. The proposed image thresholding algorithm RMIT firstly obtains a population of thresholded sub-images by using random spatial sampling and the well-known Otsu's image thresholding algorithm, then aggregates all obtained binary sub-images into a consensus binary image via majority voting. Since the sub-images are randomly selected with different sizes ranging from one pixel to the entire image, RMIT can make use of both global and local information for thresholding an image without any prior knowledge about the image. The effectiveness of RMIT is confirmed by experimental results on benchmark real images. © 2010 IEEE.

Original language	English
Title of host publication	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Publisher	IEEE
Pages	746-751
Number of pages	6
ISBN (Electronic)	9781424465279
ISBN (Print)	9781424465262
DOIs	https://doi.org/10.1109/ICMLC.2010.5580571
Publication status	Published - Jul 2010
Externally published	Yes
Event	2010 International Conference on Machine Learning and Cybernetics - Qingdao, China Duration: 11 Jul 2010 → 14 Jul 2010

Conference

Conference	2010 International Conference on Machine Learning and Cybernetics
Abbreviated title	ICMLC 2010
Country/Territory	China
City	Qingdao
Period	11/07/10 → 14/07/10

Keywords

Image thresholding
Majority voting
Random spatial sampling

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10.1109/ICMLC.2010.5580571

Cite this

@inproceedings{50e4082dbd2946e497e4c13661a7dfee,

title = "Image thresholding based on random spatial sampling and majority voting",

abstract = "This paper presents a novel image thresholding algorithm, namely Random spatial sampling and Majority voting based Image Thresholding (RMIT) algorithm. The proposed image thresholding algorithm RMIT firstly obtains a population of thresholded sub-images by using random spatial sampling and the well-known Otsu's image thresholding algorithm, then aggregates all obtained binary sub-images into a consensus binary image via majority voting. Since the sub-images are randomly selected with different sizes ranging from one pixel to the entire image, RMIT can make use of both global and local information for thresholding an image without any prior knowledge about the image. The effectiveness of RMIT is confirmed by experimental results on benchmark real images. {\textcopyright} 2010 IEEE.",

keywords = "Image thresholding, Majority voting, Random spatial sampling",

author = "Yi HONG and Hanli WANG and Sam KWONG",

year = "2010",

month = jul,

doi = "10.1109/ICMLC.2010.5580571",

language = "English",

isbn = "9781424465262",

pages = "746--751",

booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

publisher = "IEEE",

note = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

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TY - GEN

T1 - Image thresholding based on random spatial sampling and majority voting

AU - HONG, Yi

AU - WANG, Hanli

AU - KWONG, Sam

PY - 2010/7

Y1 - 2010/7

N2 - This paper presents a novel image thresholding algorithm, namely Random spatial sampling and Majority voting based Image Thresholding (RMIT) algorithm. The proposed image thresholding algorithm RMIT firstly obtains a population of thresholded sub-images by using random spatial sampling and the well-known Otsu's image thresholding algorithm, then aggregates all obtained binary sub-images into a consensus binary image via majority voting. Since the sub-images are randomly selected with different sizes ranging from one pixel to the entire image, RMIT can make use of both global and local information for thresholding an image without any prior knowledge about the image. The effectiveness of RMIT is confirmed by experimental results on benchmark real images. © 2010 IEEE.

AB - This paper presents a novel image thresholding algorithm, namely Random spatial sampling and Majority voting based Image Thresholding (RMIT) algorithm. The proposed image thresholding algorithm RMIT firstly obtains a population of thresholded sub-images by using random spatial sampling and the well-known Otsu's image thresholding algorithm, then aggregates all obtained binary sub-images into a consensus binary image via majority voting. Since the sub-images are randomly selected with different sizes ranging from one pixel to the entire image, RMIT can make use of both global and local information for thresholding an image without any prior knowledge about the image. The effectiveness of RMIT is confirmed by experimental results on benchmark real images. © 2010 IEEE.

KW - Image thresholding

KW - Majority voting

KW - Random spatial sampling

UR - http://www.scopus.com/inward/record.url?scp=78149351891&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2010.5580571

DO - 10.1109/ICMLC.2010.5580571

M3 - Conference paper (refereed)

SN - 9781424465262

SP - 746

EP - 751

BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

PB - IEEE

T2 - 2010 International Conference on Machine Learning and Cybernetics

Y2 - 11 July 2010 through 14 July 2010

ER -

Image thresholding based on random spatial sampling and majority voting

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