A linear time implementation of k-means for multilevel thresholding of grayscale images

Pablo Fonseca, Jacques Wainer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we present a method based on the k-means algorithm for multilevel thresholding of grayscale images. The clustering is computed over the histogram rather than on the full list of intensity levels. Our implementation runs in linear time per iteration proportional to the number of bins of the histogram, not depending on the size of the image nor on the number of clusters/levels as in a traditional implementation. Therefore, it is possible to get a large speedup when the number of bins of the histogram is significantly shorter than the number of pixels. In order to achieve that running time, two restrictions were exploited in our implementation: (I) we target only grayscale images and (II) thresholding does not use spatial information.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
EditorsEduardo Bayro-Corrochano, Edwin Hancock
PublisherSpringer Verlag
Pages120-126
Number of pages7
ISBN (Electronic)9783319125671
DOIs
StatePublished - 2014
Externally publishedYes
Event19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, Mexico
Duration: 2 Nov 20145 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8827
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
Country/TerritoryMexico
CityPuerto Vallarta
Period2/11/145/11/14

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