Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910589506289664 |
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| author | Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas |
| author_facet | Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas |
| contents | 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 several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12912 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas Computer Vision and Pattern Recognition Quantum Physics 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 several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime. |
| title | Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation |
| topic | Computer Vision and Pattern Recognition Quantum Physics |
| url | https://arxiv.org/abs/2311.12912 |