skip to main content
10.1145/3377929.3398124acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Colour quantisation using self-organizing migrating algorithm

Published: 08 July 2020 Publication History

Abstract

Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Selecting these colour, which comprise a colour palette, is a challenging task since they determine the resulting image quality. In this paper, we propose a novel colour quantisation algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular, SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA. SOMA T3A works, iteratively in three phases, namely organization, migration, and update, and performs adaptive parameter definition. Migrants are selected from the population and move towards a leader during the organization process. Experimental results on a benchmark set of images show excellent colour quantisation performance and our approach to outperform several conventional and soft-computing-based colour quantisation algorithms.

References

[1]
D. Davendra and I. Zelinka (Eds.). 2016. Self-Organizing Migrating Algorithm - Methodology and Implementation. Springer.
[2]
A. H. Dekker. 1994. Kohonen neural networks for optimal colour quantization. Network: Computation in Neural Systems 5 (1994), 351--367.
[3]
Q. B. Diep. 2019. Self-organizing migrating algorithm Team To Team adaptive - SOMA T3A. In IEEE Congress on Evolutionary Computation. 1182--1187.
[4]
M. Gervautz and W. Purgathofer. 1990. A Simple Method for Color Quantization: Octree Quantization. In Graphics Gems, A. S. Glassner (Ed.). 287--293.
[5]
P. S. Heckbert. 1982. Color Image Quantization for Frame Buffer Display. ACM Computer Graphics (ACM SIGGRAPH '82 Proceedings) 16, 3 (1982), 297--307.
[6]
A. Khaled, R. F. Abdel-Kader, and M. S. Yasein. 2016. A hybrid color image quantization algorithm based on k-means and harmony search algorithms. Applied Artificial Intelligence 30, 4 (2016), 331--351.
[7]
K. Price, N. Awad, M. Ali, and P. Suganthan. 2018. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical Report. Nanyang Technological University.
[8]
G. Schaefer. 2014. Soft computing-based colour quantisation. EURASIP Journal on Image and Video Processing 2014, 1 (2014), 1--9.
[9]
G. Schaefer, Q. Hu, H. Zhou, J. F. Peters, and A. E. Hassanien. 2012. Rough c-means and fuzzy rough c-means for colour quantisation. Fundamenta Informaticae 119, 1 (2012), 113--120.
[10]
G. Schaefer and L. Nolle. 2015. A hybrid colour quantisation algorithm incorporating a human visual perception model. Computational Intelligence 31, 4 (2015), 684--698.
[11]
G. Schaefer and H. Zhou. 2009. Fuzzy clustering for colour reduction in images. Telecommunication Systems 40, 1--2 (2009), 17--25.
[12]
G. Schaefer, H. Zhou, M. E. Celebi, and A. E. Hassanien. 2011. Rough colour quantisation. International Journal of Hybrid Intelligent Systems 8, 1 (2011), 25--30.
[13]
P. Scheunders. 1997. A comparison of clustering algorithms applied to color image quantization. Pattern Recognition Letters 18, 11-13 (1997), 1379--1384.
[14]
P. Scheunders. 1997. A genetic c-means clustering algorithm applied to color image quantization. Pattern Recognition 30, 6 (1997), 859--866.
[15]
I. Zelinka and J. Lampinen. 2000. SOMA - self-organizing migrating algorithm. In 6th International Conference on Soft Computing.
[16]
X. Zhang and B. A. Wandell. 1998. Color image fidelity metrics evaluated using image distortion maps. Signal processing 70, 3 (1998), 201--214.

Cited By

View all
  • (2024)Metaheuristic-based energy-aware image compression for mobile app developmentMultimedia Tools and Applications10.1007/s11042-024-19256-yOnline publication date: 2-May-2024
  • (2022)Comparison of Various Machine Learning Algorithms on Color Quantization Techniques2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)10.1109/CCET56606.2022.10080406(1-10)Online publication date: 23-Dec-2022
  • (2022)Self-organizing migrating algorithm: review, improvements and comparisonArtificial Intelligence Review10.1007/s10462-022-10167-856:1(101-172)Online publication date: 4-Apr-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
July 2020
1982 pages
ISBN:9781450371278
DOI:10.1145/3377929
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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. SOMA
  2. colour quantisation
  3. optimisation

Qualifiers

  • Research-article

Funding Sources

Conference

GECCO '20
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Metaheuristic-based energy-aware image compression for mobile app developmentMultimedia Tools and Applications10.1007/s11042-024-19256-yOnline publication date: 2-May-2024
  • (2022)Comparison of Various Machine Learning Algorithms on Color Quantization Techniques2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)10.1109/CCET56606.2022.10080406(1-10)Online publication date: 23-Dec-2022
  • (2022)Self-organizing migrating algorithm: review, improvements and comparisonArtificial Intelligence Review10.1007/s10462-022-10167-856:1(101-172)Online publication date: 4-Apr-2022
  • (2020)One-array Differential Evolution Algorithm with a Novel Replacement Strategy for Numerical Optimization2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283154(2514-2519)Online publication date: 11-Oct-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media