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Color Quantization with Magnitude Sensitive Competitive Learning Algorithm

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Transactions on Computational Collective Intelligence XVII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8790))

Abstract

In this paper we introduce a competitive neural model called Magnitude Sensitive Competitive Learning (MSCL) for Color-Quantization. The aim is to obtain a codification of the color palette taking into account some specific regions of interest in the image, such as salient area, center of the image, etc. MSCL algorithm allows distributing color vector prototypes in the desired data regions according to a magnitude function. This magnitude function can allocate the codewords (colors of the palette) not only in relation to their frequency but also in response to any other data-dependent magnitude tailored to the specific goal. As we show in five different examples in this paper, MSCL is able to surpass the performance of other standard Color Quantization algorithms.

This work is partially supported by Spanish Grant TIN2010-20177 (MICINN) and FEDER and by the regional government DGA-FSE.

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Authors and Affiliations

  1. Aragon Institute for Engineering Research, University of Zaragoza, Zaragoza, Spain

    Enrique Pelayo, David Buldain & Carlos Orrite

Authors
  1. Enrique Pelayo
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  2. David Buldain
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  3. Carlos Orrite
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Corresponding author

Correspondence to Enrique Pelayo .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Melbourne, Australia and Polish Academy of Sciences, Warsaw, Poland

    Ryszard Kowalczyk

  3. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  4. Polytechnic Institute of Sétubal, Sétubal, Portugal

    Filipe Joaquim

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Pelayo, E., Buldain, D., Orrite, C. (2014). Color Quantization with Magnitude Sensitive Competitive Learning Algorithm. In: Nguyen, N., Kowalczyk, R., Fred, A., Joaquim, F. (eds) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science(), vol 8790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44994-3_11

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  • DOI: https://doi.org/10.1007/978-3-662-44994-3_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44993-6

  • Online ISBN: 978-3-662-44994-3

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