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A Comprehensive Review and New Taxonomy on Superpixel Segmentation

Authors:

Isabela Borlido Barcelos,

Felipe De Castro Belém,

Leonardo De Melo João,

Zenilton K. G. Do Patrocínio, Jr.,

Alexandre Xavier Falcão,

Silvio Jamil Ferzoli GuimarãesAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 8

Article No.: 200, Pages 1 - 39

https://doi.org/10.1145/3652509

Published: 10 April 2024 Publication History

Abstract

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 a 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 23 strategies and a grid segmentation baseline 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 superpixel segmentation and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.

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Li NXiang DSun XHu CSu Y(2025)Multiscale adaptive PolSAR image superpixel generation based on local iterative clustering and polarimetric scattering featuresISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.12.011220(307-322)Online publication date: Feb-2025
https://doi.org/10.1016/j.isprsjprs.2024.12.011
Zhang CLiu XYao ABai JDong CPal SJiang F(2024)Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV LogisticsDrones10.3390/drones80703128:7(312)Online publication date: 10-Jul-2024
https://doi.org/10.3390/drones8070312
Wang CDuan WLuan CLiang JShen LLi H(2024)USNet: underwater image superpixel segmentation via multi-scale water-netFrontiers in Marine Science10.3389/fmars.2024.141171711Online publication date: 23-May-2024
https://doi.org/10.3389/fmars.2024.1411717
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Index Terms

A Comprehensive Review and New Taxonomy on Superpixel Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 8

August 2024

963 pages

EISSN:1557-7341

DOI:10.1145/3613627

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2024

Online AM: 12 March 2024

Accepted: 07 March 2024

Revised: 20 February 2024

Received: 09 February 2023

Published in CSUR Volume 56, Issue 8

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Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq
Fundação de Amparo a Pesquisa do Estado de Minas Gerais—FAPEMIG
Fundação de Amparo a Pesquisa do Estado de São Paulo—FAPESP
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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Cited By

Li NXiang DSun XHu CSu Y(2025)Multiscale adaptive PolSAR image superpixel generation based on local iterative clustering and polarimetric scattering featuresISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.12.011220(307-322)Online publication date: Feb-2025
https://doi.org/10.1016/j.isprsjprs.2024.12.011
Zhang CLiu XYao ABai JDong CPal SJiang F(2024)Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV LogisticsDrones10.3390/drones80703128:7(312)Online publication date: 10-Jul-2024
https://doi.org/10.3390/drones8070312
Wang CDuan WLuan CLiang JShen LLi H(2024)USNet: underwater image superpixel segmentation via multi-scale water-netFrontiers in Marine Science10.3389/fmars.2024.141171711Online publication date: 23-May-2024
https://doi.org/10.3389/fmars.2024.1411717
Lopes GFonseca GNovais GFilho DSouto GRohde GPatrocínio ZFalcão AGuimarães S(2024)Cell Classification Based on Superpixel Segmentation and Transport-Based Morphometry2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI62404.2024.10716334(1-6)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SIBGRAPI62404.2024.10716334
Borlido IBouhid ESundermann VResende HFazenda AFaria FGuimar S(2024)How to Identify Good Superpixels for Deforestation Detection on Tropical RainforestsIEEE Geoscience and Remote Sensing Letters10.1109/LGRS.2024.345497321(1-5)Online publication date: 2024
https://doi.org/10.1109/LGRS.2024.3454973
Vieira DBarcelos IPatrocínio Jr ZFalcão AGuimarães S(2024)Towards Interactive Video Segmentation by Dynamic and Iterative Spanning ForestProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-031-76607-7_12(162-177)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-76607-7_12
Lacerda LBelém FJúnior ZFalcão AGuimarães S(2024)Seed-Based Superpixel Re-Segmentation for Improving Object DelineationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-031-76607-7_11(148-161)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-76607-7_11

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