DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity

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.