Enhancing Image Vectorization Fidelity and Editability through Adaptive Layered Techniques

Image vectorization transforms raster images into scalable vector graphics, offering advantages in resolution independence and compact representation. Traditional and modern machine learning-based methods face challenges in redundancy, structural clarity, computational costs, and editability. To address these, we propose ALIV (Adaptive Layered Image Vectorization), a novel two-step framework combining comprehensive color quantization pretreatment with a machine learning vectorization process. ALIV enhances shape representation through adaptive polygonal primitive initialization and employs structural and curve loss functions to maintain clear boundaries and reduce disordered curves. Our approach achieves high fidelity, superior structural quality, and enhanced editability while maintaining computational efficiency. Experiments demonstrate ALIV's superiority over state-of-the-art methods, significantly reducing computational overhead and ensuring better vectorization results. This work contributes to the field of computer graphics by providing a practical and efficient solution for image vectorization. The source code for ALIV are publicly available at https://github.com/SamEr32/ALIV (DOI:10.5281/zenodo.17311513).