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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.0 20120330//EN" "JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article"><front><journal-meta><journal-id journal-id-type="publisher-id">INFORMATICA</journal-id><journal-title-group><journal-title>Informatica</journal-title></journal-title-group><issn pub-type="epub">0868-4952</issn><issn pub-type="ppub">0868-4952</issn><publisher><publisher-name>VU</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">inf25308</article-id><article-id pub-id-type="doi">10.15388/Informatica.2014.25</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research article</subject></subj-group></article-categories><title-group><article-title>Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm</article-title></title-group><contrib-group><contrib contrib-type="Author"><name><surname>Ozturk</surname><given-names>Celal</given-names></name><email xlink:href="mailto:celal@erciyes.edu.tr">celal@erciyes.edu.tr</email><xref ref-type="aff" rid="j_INFORMATICA_aff_000"/><xref ref-type="corresp" rid="fn1">∗</xref></contrib><contrib contrib-type="Author"><name><surname>Hancer</surname><given-names>Emrah</given-names></name><email xlink:href="mailto:emrahhancer@erciyes.edu.tr">emrahhancer@erciyes.edu.tr</email><xref ref-type="aff" rid="j_INFORMATICA_aff_000"/></contrib><contrib contrib-type="Author"><name><surname>Karaboga</surname><given-names>Dervis</given-names></name><email xlink:href="mailto:karaboga@erciyes.edu.tr">karaboga@erciyes.edu.tr</email><xref ref-type="aff" rid="j_INFORMATICA_aff_000"/></contrib><aff id="j_INFORMATICA_aff_000">Department of Computer Engineering, Erciyes University, Kayseri, 38039, Turkey</aff></contrib-group><author-notes><corresp id="fn1"><label>∗</label>Corresponding author.</corresp></author-notes><pub-date pub-type="epub"><day>01</day><month>01</month><year>2014</year></pub-date><volume>25</volume><issue>3</issue><fpage>485</fpage><lpage>503</lpage><history><date date-type="received"><day>01</day><month>12</month><year>2012</year></date><date date-type="accepted"><day>01</day><month>07</month><year>2013</year></date></history><abstract><p>Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.</p></abstract><kwd-group><label>Keywords</label><kwd>color quantization</kwd><kwd>artificial bee colony</kwd><kwd>particle swarm optimization</kwd><kwd>K-means</kwd><kwd>fuzzy C means</kwd></kwd-group></article-meta></front></article>