# Power-SLIC: Fast Superpixel Segmentations by Diagrams

arXiv: https://arxiv.org/abs/2012.11772 · HTML: https://mlsanity.com/paper/2012.11772/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Superpixel Generation using Edge-Sparse Embedding](https://mlsanity.com/paper/2211.15474/) (2022)
- [Fitting Generalized Power Diagrams to 3D Image Data: A Prerequisite for Virtual Materials Testing](https://mlsanity.com/paper/2507.14268/) (2025)
- [Superpixel-Based Image Segmentation Using Squared 2-Wasserstein Distances](https://mlsanity.com/paper/2601.17071/) (2026)

## Abstract (arXiv)

Superpixel algorithms grouping pixels with similar color and other low-level properties are increasingly used for pre-processing in image segmentation. In recent years, a focus has been placed on developing geometric superpixel methods that facilitate the extraction and analysis of geometric image features. Diagram-based superpixel methods are important among the geometric methods as they generate compact and sparsely representable superpixels. Introducing generalized balanced power diagrams to the field of superpixels, we propose a diagram method called Power-SLIC. Power-SLIC is the first geometric superpixel method to generate piecewise quadratic boundaries. Its speed, competitive with fast state-of-the-art methods, is unprecedented for diagram approaches. Extensive computational experiments show that Power-SLIC outperforms existing diagram approaches in boundary recall, under segmentation error, achievable segmentation accuracy, and compression quality. Moreover, Power-SLIC is robust to Gaussian noise.