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Hierarchical Colour Image Segmentation by Leveraging RGB Channels Independently

conference contribution
posted on 2022-11-25, 03:30 authored by S Tania, M Murshed, SW Teng, G Karmakar
In this paper, we introduce a hierarchical colour image segmentation based on cuboid partitioning using simple statistical features of the pixel intensities in the RGB channels. Estimating the difference between any two colours is a challenging task. As most of the colour models are not perceptually uniform, investigation of an alternative strategy is highly demanding. To address this issue, for our proposed technique, we present a new concept for colour distance measure based on the inconsistency of pixel intensities of an image which is more compliant to human perception. Constructing a reliable set of superpixels from an image is fundamental for further merging. As cuboid partitioning is a superior candidate to produce superpixels, we use the agglomerative merging to yield the final segmentation results exploiting the outcome of our proposed cuboid partitioning. The proposed cuboid segmentation based algorithm significantly outperforms not only the quadtree-based segmentation but also existing state-of-the-art segmentation algorithms in terms of quality of segmentation for the benchmark datasets used in image segmentation.

History

Volume

11854 LNCS

Pagination

197-210

Location

Sydney, AUSTRALIA

Start date

2019-11-18

End date

2019-11-22

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030348786

Language

English

Editor/Contributor(s)

Sugimoto A

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event

9th Pacific-Rim Symposium on Image and Video Technology (PSIVT)

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Series

Lecture Notes in Computer Science

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