Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation
This provides a quantum computing solution for image segmentation, particularly in domains like earth observation with noisy data and limited labels, though it appears incremental as it builds on existing quantum hardware and graph-cut methods.
Q-Seg tackles unsupervised image segmentation by formulating it as a graph-cut optimization problem and using quantum annealing on D-Wave hardware, achieving better runtime performance than the classical optimizer Gurobi on synthetic datasets.
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.