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arXiv · 2305.07954

Image Segmentation via Probabilistic Graph Matching

Ayelet Heimowitz·Yosi Keller
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Abstract

This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low level image cues and automatic tuning of parameters. The proposed scheme is experimentally shown to compare favorably with contemporary semi-supervised and unsupervised image segmentation schemes, when applied to contemporary state-of-the-art image sets.

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Ayelet Heimowitz, Yosi Keller. 2023-05-13. Image Segmentation via Probabilistic Graph Matching. https://doi.org/10.1109/tip.2016.2590832

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