Our work tackles the fundamental challenge of image segmentation in computer vision, which is crucial for diverse applications. While supervised methods demonstrate proficiency, their reliance on extensive pixel-level annotations limits scalability. We introduce DynaSeg, an innovative unsupervised image segmentation approach that overcomes the challenge of balancing feature similarity and spatial continuity without relying on extensive hyperparameter tuning. Unlike traditional methods, DynaSeg employs a dynamic weighting scheme that automates parameter tuning, adapts flexibly to image characteristics, and facilitates easy integration with other segmentation networks. By incorporating a Silhouette Score Phase, DynaSeg prevents undersegmentation failures where the number of predicted clusters might converge to one. DynaSeg uses CNN-based and pre-trained ResNet feature extraction, making it computationally efficient and more straightforward than other complex models. Experimental results showcase state-of-the-art performance, achieving a 12.2% and 14.12% mIOU improvement over current unsupervised segmentation approaches on COCO-All and COCO-Stuff datasets, respectively. We provide qualitative and quantitative results on five benchmark datasets, demonstrating the efficacy of the proposed approach.Code is available at https://github.com/RyersonMultimediaLab/DynaSeg

翻译：本研究致力于解决计算机视觉中图像分割这一基础性挑战，该任务对于众多应用至关重要。尽管监督方法表现出色，但其对大量像素级标注的依赖限制了可扩展性。我们提出了DynaSeg，一种创新的无监督图像分割方法，它无需依赖大量超参数调优，即可克服平衡特征相似性与空间连续性的难题。与传统方法不同，DynaSeg采用动态加权方案，可自动进行参数调优，灵活适应图像特性，并易于与其他分割网络集成。通过引入轮廓系数阶段，DynaSeg能够避免预测聚类数可能收敛为1的欠分割失败情况。DynaSeg采用基于CNN和预训练ResNet的特征提取，使其计算效率高，且比其他复杂模型更为简洁。实验结果表明了其最先进的性能，在COCO-All和COCO-Stuff数据集上，相较于当前的无监督分割方法，分别实现了12.2%和14.12%的mIOU提升。我们在五个基准数据集上提供了定性和定量结果，证明了所提方法的有效性。代码发布于 https://github.com/RyersonMultimediaLab/DynaSeg。