# A comprehensive review and new taxonomy on superpixel segmentation

arXiv: https://arxiv.org/abs/2409.19179 · HTML: https://mlsanity.com/paper/2409.19179/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 114 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Superpixel Sampling Networks](https://mlsanity.com/paper/1807.10174/) (2018)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Recurrent Pixel Embedding for Instance Grouping](https://mlsanity.com/paper/1712.08273/) (2017)
- [Superpixels: An Evaluation of the State-of-the-Art](https://mlsanity.com/paper/1612.01601/) (2016)
- [Superpixel Hierarchy](https://mlsanity.com/paper/1605.06325/) (2016)
- [Learning the Superpixel in a Non-iterative and Lifelong Manner](https://mlsanity.com/paper/2103.10681/) (2021)
- [Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1903.06548/) (2019)
- [Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization](https://mlsanity.com/paper/2002.06765/) (2020)
- [HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation](https://mlsanity.com/paper/2106.03755/) (2021)
- [Robust superpixels using color and contour features along linear path](https://mlsanity.com/paper/1903.07193/) (2019)
- [An Iterative Spanning Forest Framework for Superpixel Segmentation](https://mlsanity.com/paper/1801.10041/) (2018)
- [AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation](https://mlsanity.com/paper/2101.10696/) (2021)
- [Superpixel Segmentation using Dynamic and Iterative Spanning Forest](https://mlsanity.com/paper/2007.04257/) (2020)
- [Efficient Multiscale Object-based Superpixel Framework](https://mlsanity.com/paper/2204.03533/) (2022)
- [Peekaboo - Where are the Objects? Structure Adjusting Superpixels](https://mlsanity.com/paper/1802.02796/) (2018)
- [Dynamic Spectral Residual Superpixels](https://mlsanity.com/paper/1910.04794/) (2019)
- [Superpixel Segmentation Based on Spatially Constrained Subspace Clustering](https://mlsanity.com/paper/2012.06149/) (2020)
- [SIN:Superpixel Interpolation Network](https://mlsanity.com/paper/2110.08702/) (2021)

## Built on by (most cited first, 8 of 8)

- [Superpixel Segmentation: A Long-Lasting Ill-Posed Problem](https://mlsanity.com/paper/2411.06478/) (2024)
- [Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection](https://mlsanity.com/paper/2411.17922/) (2024)
- [Do Superpixel Segmentation Methods Influence Deforestation Image Classification?](https://mlsanity.com/paper/2510.04645/) (2025)
- [Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking](https://mlsanity.com/paper/2512.00052/) (2025)
- [Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images](https://mlsanity.com/paper/2602.24160/) (2026)
- [Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach](https://mlsanity.com/paper/2603.00127/) (2026)
- [Computer Vision with a Superpixelation Camera](https://mlsanity.com/paper/2603.26900/) (2026)
- [Compact Feed-Forward 3D Gaussians via Saliency-Guided Primitive Merging](https://mlsanity.com/paper/2608.10712/) (2026)

## Abstract (arXiv)

Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. This work fills this gap by presenting a comprehensive review with new taxonomy for superpixel segmentation, in which methods are classified according to their processing steps and processing levels of image features. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Our experiments show the trends of each approach in pixel clustering and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.