Hierarchical colour image segmentation by leveraging RGB channels independently

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

Title
Hierarchical colour image segmentation by leveraging RGB channels independently
Creator
Tania, Sheikh; Murshed, Manzur; Teng, Shyh; Karmakar, Gour
Date
2019
Type
Text; Conference paper
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171156
Identifier
vital:14285
Identifier
https://doi.org/10.1007/978-3-030-34879-3_16
Identifier
ISBN:03029743; 9783030348786
Abstract
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. © 2019, Springer Nature Switzerland AG.
Publisher
Springer
Relation
9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019 Vol. 11854 LNCS, p. 197-210
Rights
@ Springer Nature Switzerland AG
Rights
This metadata is freely available under a CCO license
Subject
Agglomerative merging; Colour image segmentation; Cuboid segmentation
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