An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction

Lulu He*, Thrasyvoulos N Pappas

*Corresponding author for this work

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

4 Scopus citations

Abstract

We present a novel, computationally efficient approach for natural image segmentation that uses the adaptive clustering algorithm (ACA) to obtain an initial segmentation and chrominance-based region merging to consolidate regions of perceptually uniform texture. The combination of ACA and chrominance-based merging preserves salient edges and smooths out noise and edges within textured regions. It can thus be used for image abstraction. Experimental results with natural images indicate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages241-244
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Adaptive clustering algorithm
  • Bilateral filtering
  • Region merging

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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    He, L., & Pappas, T. N. (2010). An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction. In 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings (pp. 241-244). [5651905] https://doi.org/10.1109/ICIP.2010.5651905