# Efficient Multiscale Object-based Superpixel Framework

arXiv: https://arxiv.org/abs/2204.03533 · HTML: https://mlsanity.com/paper/2204.03533/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Superpixel Sampling Networks](https://mlsanity.com/paper/1807.10174/) (2018)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [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)
- [Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization](https://mlsanity.com/paper/2002.06765/) (2020)
- [Superpixel Segmentation Using Gaussian Mixture Model](https://mlsanity.com/paper/1612.08792/) (2016)
- [HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation](https://mlsanity.com/paper/2106.03755/) (2021)
- [An Iterative Spanning Forest Framework for Superpixel Segmentation](https://mlsanity.com/paper/1801.10041/) (2018)
- [Superpixel Segmentation using Dynamic and Iterative Spanning Forest](https://mlsanity.com/paper/2007.04257/) (2020)

## Built on by (most cited first, 4 of 4)

- [A comprehensive review and new taxonomy on superpixel segmentation](https://mlsanity.com/paper/2409.19179/) (2024)
- [How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests](https://mlsanity.com/paper/2409.04330/) (2024)
- [Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection](https://mlsanity.com/paper/2411.17922/) (2024)
- [SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks](https://mlsanity.com/paper/2304.05661/) (2023)

## Abstract (arXiv)

Superpixel segmentation can be used as an intermediary step in many applications, often to improve object delineation and reduce computer workload. However, classical methods do not incorporate information about the desired object. Deep-learning-based approaches consider object information, but their delineation performance depends on data annotation. Additionally, the computational time of object-based methods is usually much higher than desired. In this work, we propose a novel superpixel framework, named Superpixels through Iterative CLEarcutting (SICLE), which exploits object information being able to generate a multiscale segmentation on-the-fly. SICLE starts off from seed oversampling and repeats optimal connectivity-based superpixel delineation and object-based seed removal until a desired number of superpixels is reached. It generalizes recent superpixel methods, surpassing them and other state-of-the-art approaches in efficiency and effectiveness according to multiple delineation metrics.