# Effective Clipart Image Vectorization Through Direct Optimization of Bezigons

arXiv: https://arxiv.org/abs/1602.01913 · HTML: https://mlsanity.com/paper/1602.01913/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2016.

## Built on by (most cited first, 11 of 11)

- [Towards Layer-wise Image Vectorization](https://mlsanity.com/paper/2206.04655/) (2022)
- [Text-to-Vector Generation with Neural Path Representation](https://mlsanity.com/paper/2405.10317/) (2024)
- [Layered Image Vectorization via Semantic Simplification](https://mlsanity.com/paper/2406.05404/) (2024)
- [Segmentation-guided Layer-wise Image Vectorization with Gradient Fills](https://mlsanity.com/paper/2408.15741/) (2024)
- [LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization](https://mlsanity.com/paper/2505.23740/) (2025)
- [Image Vectorization: a Review](https://mlsanity.com/paper/2306.06441/) (2023)
- [SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model](https://mlsanity.com/paper/2311.05276/) (2023)
- [Text-Guided Vector Graphics Customization](https://mlsanity.com/paper/2309.12302/) (2023)
- [An Active Contour Model for Silhouette Vectorization using B'ezier Curves](https://mlsanity.com/paper/2505.05132/) (2025)
- [SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Icons](https://mlsanity.com/paper/2603.24039/) (2026)
- [VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation](https://mlsanity.com/paper/2605.24398/) (2026)

## Abstract (arXiv)

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.