Effective Clipart Image Vectorization Through Direct Optimization of Bezigons

TL;DR

This paper introduces a novel clipart image vectorization method that directly optimizes bezigons, resulting in higher fidelity and more accurate vector representations compared to existing techniques.

Contribution

It proposes a direct bezigon optimization approach with differentiable energy and curve priors, improving vectorization quality over traditional intermediate representation methods.

Findings

Outperforms state-of-the-art vectorization methods.

Produces higher fidelity bezigons closely matching expert traces.

Demonstrates superior results over commercial software.

Abstract

Bezigons, i.e., closed paths composed of B\'ezier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.