# Superpixels: An Evaluation of the State-of-the-Art

arXiv: https://arxiv.org/abs/1612.01601 · HTML: https://mlsanity.com/paper/1612.01601/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2016.

## Built on by (most cited first, 30 of 52)

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- [Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels](https://mlsanity.com/paper/2201.12633/) (2022)
- [Active Learning for Semantic Segmentation with Multi-class Label Query](https://mlsanity.com/paper/2309.09319/) (2023)
- [A comprehensive review and new taxonomy on superpixel segmentation](https://mlsanity.com/paper/2409.19179/) (2024)
- [Superevents: Towards Native Semantic Segmentation for Event-based Cameras](https://mlsanity.com/paper/2105.06091/) (2021)
- [Component Segmentation of Engineering Drawings Using Graph Convolutional Networks](https://mlsanity.com/paper/2212.00290/) (2022)
- [Learning Invariant Inter-pixel Correlations for Superpixel Generation](https://mlsanity.com/paper/2402.18201/) (2024)
- [Superpixel-based Refinement for Object Proposal Generation](https://mlsanity.com/paper/2101.04574/) (2021)
- [Efficient Multiscale Object-based Superpixel Framework](https://mlsanity.com/paper/2204.03533/) (2022)
- [BoxShrink: From Bounding Boxes to Segmentation Masks](https://mlsanity.com/paper/2208.03142/) (2022)
- [DeepFH Segmentations for Superpixel-based Object Proposal Refinement](https://mlsanity.com/paper/2108.03503/) (2021)
- [A Novel Explainable Artificial Intelligence Model in Image Classification problem](https://mlsanity.com/paper/2307.04137/) (2023)
- [Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network](https://mlsanity.com/paper/2308.03382/) (2023)
- [Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation](https://mlsanity.com/paper/2509.12791/) (2025)
- [Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks](https://mlsanity.com/paper/2103.03435/) (2021)
- [SIN:Superpixel Interpolation Network](https://mlsanity.com/paper/2110.08702/) (2021)
- [GraphVid: It Only Takes a Few Nodes to Understand a Video](https://mlsanity.com/paper/2207.01375/) (2022)
- [Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection](https://mlsanity.com/paper/2303.14322/) (2023)
- [Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms](https://mlsanity.com/paper/2309.13438/) (2023)
- [Superpixel Segmentation: A Long-Lasting Ill-Posed Problem](https://mlsanity.com/paper/2411.06478/) (2024)
- [The Weakly-Labeled Rand Index](https://mlsanity.com/paper/2103.04872/) (2021)

## Abstract (arXiv)

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/.