Layerwise Image Vectorization via Bayesain-Optimized Contour

Ghfran Jabour, Sergey Muravyov, Valeria Efimova

Abstract: This work presents a novel method LIVBOC for complex image vectorization that addresses key challenges in path initialization, color assignment, and optimization. Unlike existing approaches such as LIVE, our method generates Bayesian-optimized contour for path initialization, which is then optimized using a customized loss function to align it better with the target shape in the image. In our method, adaptive selection of points and parameters for efficient and accurate vectorization is enabled to reduce unnecessary iterations and computational overhead. LIVBOC achieves superior reconstruction fidelity with fewer paths, and that is due to the path initialization technique, which initializes paths as contours that approximate target shapes in the image, reducing redundancy in points and paths. The experimental evaluation indicates that LIVBOC outperforms LIVE in all key metrics, including a significant reduction in L2 loss, processing time, and file size. LIVBOC achieves comparable re

External IDs:dblp:conf/visigrapp/JabourME25