NDLI: Human perception-based image segmentation using optimising of colour quantisation

Content Provider	IET Digital Library
Author	Cho, Sung In Kang, Suk Ju Kim, Young Hwan
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.
Starting Page	761
Ending Page	770
Page Count	10
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 12, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0602
Journal	IET Image Processing
Publisher Date	2014-06-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Advanced Histogram-based Image Segmentation Method Berkeley Segmentation Dataset Clustering Centroids Colour Histogram Colour Quantisation Optimization Computational Complexity Computer Vision And Image Processing Technique Connected-component Labelling Connectivity Analysis Human Perception-based Image Segmentation Image Colour Analysis Image Enhancement Image Segmentation Image Segmentation Quality Enhancement Optical, Image And Video Signal Processing Over-segmentation Removal Technique Pattern Clustering Probabilistic Rand Index Probability Quantisation (signal) Segmentation Covering Value Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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