Segmentation of Multi-Band Images Using Watershed Arcs

Watershed Arcs Removal for node-weighted graphs method addressed the over-segmentation problem of classical watershed transformation, in a significantly shorter run-time. In this study, a variation of Watershed Arcs Removal is proposed that generates hierarchical partitioning in an edge-weighted gra...

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Bibliographic Details
Published in:IEEE signal processing letters 2022, Vol.29, p.2407-2411
Main Authors: Soor, Sampriti, Sagar, B. S. Daya
Format: Article
Language:English
Subjects:
Costs
Image edge detection
Image segmentation
Merging
Object detection
Pattern recognition
region merging
Target recognition
watershed arcs
Watershed transformation
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Summary:Watershed Arcs Removal for node-weighted graphs method addressed the over-segmentation problem of classical watershed transformation, in a significantly shorter run-time. In this study, a variation of Watershed Arcs Removal is proposed that generates hierarchical partitioning in an edge-weighted graph. In the proposed method, regions are grown from the nodes having high local similarity to find the initial arcs, and neighbouring regions are merged by gradually removing arcs with low local dissimilarity. The arcs to be removed in a level are selected solely from the arc-graph constructed from the existing arcs in the previous level, weighted by their local dissimilarity. In contrast to the node-weighted variation, a strategy is employed here to preserve the critical arcs. Although the proposed method can be effectively applied to any multi-band image by transforming it into an edge-weighted graph, in this study we evaluated its performance particularly in RGB image segmentation.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3223625