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. In response to this challenge, we present an enhanced unsupervised Convolutional Neural Network (CNN)-based algorithm called DynaSeg. Unlike traditional approaches that rely on a fixed weight factor to balance feature similarity and spatial continuity, requiring manual adjustments, our novel, dynamic weighting scheme automates parameter tuning, adapting flexibly to image details. We also introduce the novel concept of a Silhouette Score Phase that addresses the challenge of dynamic clustering during iterations. Additionally, our methodology integrates both CNN-based and pre-trained ResNet feature extraction, offering a comprehensive and adaptable approach. We achieve state-of-the-art results on diverse datasets, with a notable 12.2% and 14.12% mIOU improvement compared to the current benchmarks on COCO-All and COCO-Stuff, respectively. The proposed approach unlocks the potential for unsupervised image segmentation and addresses scalability concerns in real-world scenarios by obviating the need for meticulous parameter tuning.

翻译：本研究聚焦于计算机视觉中图像分割这一基础性难题，该技术对诸多应用领域至关重要。虽然监督方法展现出优越性能，但其对大规模像素级标注的依赖限制了可扩展性。针对这一挑战，我们提出了一种增强型无监督卷积神经网络（CNN）算法，命名为DynaSeg。不同于传统方法采用固定权重因子来平衡特征相似性与空间连续性（需人工调整参数），我们创新的动态权重方案实现了参数自动化调优，能灵活适应图像细节特征。我们还引入了轮廓系数阶段（Silhouette Score Phase）这一新概念，解决了迭代过程中动态聚类面临的挑战。此外，本方法融合了基于CNN的特征提取与预训练ResNet特征提取，提供了一套全面而自适应的解决方案。在多个数据集上，我们的方法达到了最优性能：与当前基准方法相比，在COCO-All和COCO-Stuff数据集上的平均交并比（mIOU）分别提升了12.2%和14.12%。该方案通过消除繁琐的参数调优需求，不仅释放了无监督图像分割的潜力，也解决了实际应用场景中的可扩展性问题。