Edge-adaptive clustering for unsupervised image segmentation

D. L. Pham

Research output: Contribution to conferencePaper

Abstract

When used for image segmentation, most standard clustering algorithms can shift image boundaries due to intensity fluctuations within an image. In this paper, a novel approach to clustering is proposed for performing unsupervised image segmentation based upon a generalization of the standard K-means clustering algorithm. By incorporating a new term into the objective function of the K-means algorithm, boundaries between regions in the resulting segmentation are forced to occur at the same locations as edges in the observed image. A straightforward iterative algorithm is derived for minimizing this edge-adaptive K-means objective function. The result is an efficient segmentation algorithm that reconstructs boundaries in the image more accurately than standard methods.

Original languageEnglish (US)
Pages816-819
Number of pages4
StatePublished - Dec 1 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Pham, D. L. (2000). Edge-adaptive clustering for unsupervised image segmentation. 816-819. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.