Normalized sampling for color clustering in medical diagnosis

C. H. Li*, Pong Chi Yuen

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


The classical approach of using minimum cut criterion for clustering is often ineffective due to the existence of outliers in the data. This paper presents a novel normalized graph sampling algorithm for clustering that improves the solution of clustering via the incorporation of a priori constraint in a stochastic graph sampling procedure. The quality of the proposed algorithm is empirically evaluated on two synthetic datasets and a color medical image database.

Original languageEnglish
Pages (from-to)819-822
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number3
Publication statusPublished - 2002

Scopus Subject Areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Normalized sampling for color clustering in medical diagnosis'. Together they form a unique fingerprint.

Cite this