Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
- URL: http://arxiv.org/abs/2311.12912v2
- Date: Thu, 30 Nov 2023 11:38:07 GMT
- Title: Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
- Authors: Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske, Matthias
Klusch, Andreas Dengel
- Abstract summary: We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware.
Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches.
Our empirical evaluations on synthetic datasets reveal that Q-Seg offers better runtime performance against the classical Gurobi.
- Score: 5.082936769620375
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this study, we present Q-Seg, a novel unsupervised image segmentation
method based on quantum annealing, tailored for existing quantum hardware. We
formulate the pixel-wise segmentation problem, which assimilates spectral and
spatial information of the image, as a graph-cut optimization task. Our method
efficiently leverages the interconnected qubit topology of the D-Wave Advantage
device, offering superior scalability over existing quantum approaches and
outperforming state-of-the-art classical methods. Our empirical evaluations on
synthetic datasets reveal that Q-Seg offers better runtime performance against
the classical optimizer Gurobi. Furthermore, we evaluate our method on
segmentation of Earth Observation images, an area of application where the
amount of labeled data is usually very limited. In this case, Q-Seg
demonstrates near-optimal results in flood mapping detection with respect to
classical supervised state-of-the-art machine learning methods. Also, Q-Seg
provides enhanced segmentation for forest coverage compared to existing
annotated masks. Thus, Q-Seg emerges as a viable alternative for real-world
applications using available quantum hardware, particularly in scenarios where
the lack of labeled data and computational runtime are critical.
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