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Perceptual dominant color extraction by multidimensional particle swarm optimization

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Standard

Perceptual dominant color extraction by multidimensional particle swarm optimization. / Kiranyaz, Serkan; Uhlmann, Stefan; Ince, Turker; Gabbouj, Moncef.

julkaisussa: Eurasip Journal on Advances in Signal Processing, Vuosikerta 2009, Nro 451638, 2009, s. 1-13.

Tutkimustuotosvertaisarvioitu

Harvard

Kiranyaz, S, Uhlmann, S, Ince, T & Gabbouj, M 2009, 'Perceptual dominant color extraction by multidimensional particle swarm optimization', Eurasip Journal on Advances in Signal Processing, Vuosikerta. 2009, Nro 451638, Sivut 1-13. https://doi.org/10.1155/2009/451638

APA

Kiranyaz, S., Uhlmann, S., Ince, T., & Gabbouj, M. (2009). Perceptual dominant color extraction by multidimensional particle swarm optimization. Eurasip Journal on Advances in Signal Processing, 2009(451638), 1-13. https://doi.org/10.1155/2009/451638

Vancouver

Kiranyaz S, Uhlmann S, Ince T, Gabbouj M. Perceptual dominant color extraction by multidimensional particle swarm optimization. Eurasip Journal on Advances in Signal Processing. 2009;2009(451638):1-13. https://doi.org/10.1155/2009/451638

Author

Kiranyaz, Serkan ; Uhlmann, Stefan ; Ince, Turker ; Gabbouj, Moncef. / Perceptual dominant color extraction by multidimensional particle swarm optimization. Julkaisussa: Eurasip Journal on Advances in Signal Processing. 2009 ; Vuosikerta 2009, Nro 451638. Sivut 1-13.

Bibtex - Lataa

@article{54271ff13e4f43a88bc6daf1762547fe,
title = "Perceptual dominant color extraction by multidimensional particle swarm optimization",
abstract = "Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.",
author = "Serkan Kiranyaz and Stefan Uhlmann and Turker Ince and Moncef Gabbouj",
note = "Contribution: organisation=sgn,FACT1=1",
year = "2009",
doi = "10.1155/2009/451638",
language = "English",
volume = "2009",
pages = "1--13",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer International Publishing AG",
number = "451638",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Perceptual dominant color extraction by multidimensional particle swarm optimization

AU - Kiranyaz, Serkan

AU - Uhlmann, Stefan

AU - Ince, Turker

AU - Gabbouj, Moncef

N1 - Contribution: organisation=sgn,FACT1=1

PY - 2009

Y1 - 2009

N2 - Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

AB - Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

U2 - 10.1155/2009/451638

DO - 10.1155/2009/451638

M3 - Article

VL - 2009

SP - 1

EP - 13

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

IS - 451638

ER -