Color quantization using modified artificial fish swarm algorithm

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

doi:10.1007/978-3-642-25832-9_39

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 proceeding › Conference Paper › Research › peer-review

18 Citations (Scopus)

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 language	English
Title of host publication	AI 2011
Subtitle of host publication	Advances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings
Publisher	Springer
Pages	382-391
Number of pages	10
ISBN (Print)	9783642258312
DOIs	https://doi.org/10.1007/978-3-642-25832-9_39
Publication status	Published - 2011
Externally published	Yes
Event	Australasian Joint Conference on Artificial Intelligence 2011 - Perth, Australia Duration: 5 Dec 2011 → 8 Dec 2011 Conference number: 24th https://link.springer.com/book/10.1007/978-3-642-25832-9 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	7106
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Australasian Joint Conference on Artificial Intelligence 2011
Abbreviated title	AI 2011
Country/Territory	Australia
City	Perth
Period	5/12/11 → 8/12/11
Internet address	https://link.springer.com/book/10.1007/978-3-642-25832-9 (Proceedings)

Keywords

artificial fish swarm algorithm
Color quantization
compression
data clustering

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10.1007/978-3-642-25832-9_39

Cite this

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title = "Color quantization using modified artificial fish swarm algorithm",

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.",

keywords = "artificial fish swarm algorithm, Color quantization, compression, data clustering",

author = "Danial Yazdani and Hadi Nabizadeh and {Mohamadzadeh Kosari}, Elyas and {Nadjaran Toosi}, Adel",

year = "2011",

doi = "10.1007/978-3-642-25832-9_39",

language = "English",

isbn = "9783642258312",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

pages = "382--391",

booktitle = "AI 2011",

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url = "https://link.springer.com/book/10.1007/978-3-642-25832-9",

}

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, Australasian Joint Conference on Artificial Intelligence 2011, Perth, Western Australia, 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 et al.
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 proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Color quantization using modified artificial fish swarm algorithm

AU - Yazdani, Danial

AU - Nabizadeh, Hadi

AU - Mohamadzadeh Kosari, Elyas

AU - Nadjaran Toosi, Adel

N1 - Conference code: 24th

PY - 2011

Y1 - 2011

N2 - 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.

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.

KW - artificial fish swarm algorithm

KW - Color quantization

KW - compression

KW - data clustering

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Color quantization using modified artificial fish swarm algorithm

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Keywords

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