# Superpixel Hierarchy

arXiv: https://arxiv.org/abs/1605.06325 · HTML: https://mlsanity.com/paper/1605.06325/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2016.

## Built on by (most cited first, 14 of 14)

- [Learning Hierarchical Image Segmentation For Recognition and By Recognition](https://mlsanity.com/paper/2210.00314/) (2022)
- [HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation](https://mlsanity.com/paper/2106.03755/) (2021)
- [A comprehensive review and new taxonomy on superpixel segmentation](https://mlsanity.com/paper/2409.19179/) (2024)
- [Efficient Multiscale Object-based Superpixel Framework](https://mlsanity.com/paper/2204.03533/) (2022)
- [How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests](https://mlsanity.com/paper/2409.04330/) (2024)
- [Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation](https://mlsanity.com/paper/2509.12791/) (2025)
- [Superpixel Segmentation: A Long-Lasting Ill-Posed Problem](https://mlsanity.com/paper/2411.06478/) (2024)
- [InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images](https://mlsanity.com/paper/2108.13865/) (2021)
- [Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection](https://mlsanity.com/paper/2411.17922/) (2024)
- [Minimizing false negative rate in melanoma detection and providing insight into the causes of classification](https://mlsanity.com/paper/2102.09199/) (2021)
- [Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis](https://mlsanity.com/paper/2407.15321/) (2024)
- [Generalized Shortest Path-based Superpixels for 3D Spherical Image Segmentation](https://mlsanity.com/paper/2509.19895/) (2025)
- [Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images](https://mlsanity.com/paper/2602.24160/) (2026)
- [H-SPAM: Hierarchical Superpixel Anything Model](https://mlsanity.com/paper/2604.11218/) (2026)

## Abstract (arXiv)

Superpixel segmentation is becoming ubiquitous in computer vision. In practice, an object can either be represented by a number of segments in finer levels of detail or included in a surrounding region at coarser levels of detail, and thus a superpixel segmentation hierarchy is useful for applications that require different levels of image segmentation detail depending on the particular image objects segmented. Unfortunately, there is no method that can generate all scales of superpixels accurately in real-time. As a result, a simple yet effective algorithm named Super Hierarchy (SH) is proposed in this paper. It is as accurate as the state-of-the-art but 1-2 orders of magnitude faster. The proposed method can be directly integrated with recent efficient edge detectors like the structured forest edges to significantly outperforms the state-of-the-art in terms of segmentation accuracy. Quantitative and qualitative evaluation on a number of computer vision applications was conducted, demonstrating that the proposed method is the top performer.