Unsupervised clustering approaches to color classification for color-based image code recognition

Cheolho Cheong, Gordon Bowman, Tack Don Han

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Color-vision-based applications for mobile phones has become a subject of special interest lately. It would be interesting to investigate an unsupervised, adaptive, and fast algorithm that can classify color components into color clusters. We propose a hierarchical clustering approach using a single-linkage algorithm and a k-means clustering approach to color classification for color-based image code recognition in mobile computing environments. We also measured the performance of the proposed algorithms by color channel stretch, which is a simple color-correction method. Experimental results show that the single-linkage method is more robust than previous algorithms used in experiments with varying cameras and print materials. In particular the k-means-based method with color channel stretching has the highest performance and is the most robust under varying environment conditions such as illuminants, cameras, and print materials.

Original languageEnglish
Pages (from-to)2326-2345
Number of pages20
JournalApplied Optics
Volume47
Issue number13
DOIs
Publication statusPublished - 2008 May 1

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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