Graph-based image segmentation using k-means clustering and normalised cuts

Mei Yeen Choong, Wei Leong Khong, Wei Yeang Kow, Lorita Angeline, Kenneth Tze Kin Teo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

16 Citations (Scopus)

Abstract

Image segmentation with low computational burden has been highly regarded as important goal for researchers. Various image segmentation methods are widely discussed and more noble segmentation methods are expected to be developed when there is rapid demand from the emerging machine vision field. One of the popular image segmentation methods is by using normalised cuts algorithm. It is unfavourable for a high resolution image to have its resolution reduced as high detail information is not fully made used when critical objects with weak edges is coarsened undesirably after its resolution reduced. Thus, a graph-based image segmentation method done in multistage manner is proposed here. In this paper, an experimental study based on the method is conducted. This study shows an alternative approach on the segmentation method using k-means clustering and normalised cuts in multistage manner.

Original languageEnglish
Title of host publicationProceedings - 2012 4th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2012
Pages307-312
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Computational Intelligence, Communication Systems and Networks (CICSyN) 2012 - Phuket, Thailand
Duration: 24 Jul 201226 Jul 2012
Conference number: 4th
https://ieeexplore.ieee.org/xpl/conhome/6273236/proceeding (Proceedings)

Conference

ConferenceInternational Conference on Computational Intelligence, Communication Systems and Networks (CICSyN) 2012
Abbreviated titleCICSyN 2012
CountryThailand
CityPhuket
Period24/07/1226/07/12
Internet address

Keywords

  • image segmentation
  • k-means clustering
  • normalised cuts

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