Human perception-based image segmentation using optimising of colour quantisation

Sung In Cho, Suk Ju Kang, Young Hwan Kim

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This study presents an advanced histogram-based image segmentation method that enhances image segmentation quality, while greatly reducing the computational complexity. Unlike existing histogram-based methods, the authors optimise the size of bins in the colour histogram by using human perception-based colour quantisation and the clustering centroids are selected effectively without using a complex process. Additionally, an over-segmentation removal technique based on connected-component labelling is employed. This improves the segmentation quality by connectivity analysis. A comparison between the experimental results on the Berkeley Segmentation Dataset by the proposed method and the benchmark methods demonstrated that the proposed method enhanced the segmentation quality by improving the Probabilistic Rand Index and the Segmentation Covering values compared with those of the benchmark methods. The computation time using the proposed method is reduced by up to 91.63% compared with the computation time using benchmark methods.

Original languageEnglish
Pages (from-to)761-770
Number of pages10
JournalIET Image Processing
Volume8
Issue number12
DOIs
StatePublished - 1 Dec 2014

Fingerprint

Dive into the research topics of 'Human perception-based image segmentation using optimising of colour quantisation'. Together they form a unique fingerprint.

Cite this