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Hierarchical colour image segmentation by leveraging RGB channels independently
Authors
A Levinshtein
C Chen
+25 more
D Comaniciu
E Sharon
E Tapia
G Liu
J Shi
J Shotton
J Syu
JH Ward Jr
JL Bentley
K Huang
L Grady
L Vincent
LG Ugarriza
M Meilă
OAC Linares
P Arbelaez
PF Felzenszwalb
R Achanta
R Unnikrishnan
S Bagheri
S Li
SL Horowitz
T Liu
X Wang
Y Deng
Publication date
1 January 2019
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
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
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Last time updated on 10/08/2021
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