  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.
