Color quantization using modified artificial fish swarm algorithm

Danial Yazdani, Hadi Nabizadeh, Elyas Mohamadzadeh Kosari, Adel Nadjaran Toosi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Color quantization (CQ) is one of the most important techniques in image compression and processing. Most of quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.

Original languageEnglish
Title of host publicationAI 2011
Subtitle of host publicationAdvances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings
PublisherSpringer
Pages382-391
Number of pages10
ISBN (Print)9783642258312
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event24th Australasian Joint Conference on Artificial Intelligence, AI 2011 - Perth, WA, Australia
Duration: 5 Dec 20118 Dec 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7106
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Australasian Joint Conference on Artificial Intelligence, AI 2011
CountryAustralia
CityPerth, WA
Period5/12/118/12/11

Keywords

  • artificial fish swarm algorithm
  • Color quantization
  • compression
  • data clustering

Cite this

Yazdani, D., Nabizadeh, H., Mohamadzadeh Kosari, E., & Nadjaran Toosi, A. (2011). Color quantization using modified artificial fish swarm algorithm. In AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings (pp. 382-391). (Lecture Notes in Computer Science ; Vol. 7106 ). Springer. https://doi.org/10.1007/978-3-642-25832-9_39
Yazdani, Danial ; Nabizadeh, Hadi ; Mohamadzadeh Kosari, Elyas ; Nadjaran Toosi, Adel. / Color quantization using modified artificial fish swarm algorithm. AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings. Springer, 2011. pp. 382-391 (Lecture Notes in Computer Science ).
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Yazdani, D, Nabizadeh, H, Mohamadzadeh Kosari, E & Nadjaran Toosi, A 2011, Color quantization using modified artificial fish swarm algorithm. in AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science , vol. 7106 , Springer, pp. 382-391, 24th Australasian Joint Conference on Artificial Intelligence, AI 2011, Perth, WA, Australia, 5/12/11. https://doi.org/10.1007/978-3-642-25832-9_39

Color quantization using modified artificial fish swarm algorithm. / Yazdani, Danial; Nabizadeh, Hadi; Mohamadzadeh Kosari, Elyas; Nadjaran Toosi, Adel.

AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings. Springer, 2011. p. 382-391 (Lecture Notes in Computer Science ; Vol. 7106 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - Color quantization (CQ) is one of the most important techniques in image compression and processing. Most of quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.

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Yazdani D, Nabizadeh H, Mohamadzadeh Kosari E, Nadjaran Toosi A. Color quantization using modified artificial fish swarm algorithm. In AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings. Springer. 2011. p. 382-391. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-642-25832-9_39