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17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Fast Color Image Quantization using Squared Euclidean Distance of Adjacent Color Points along the Highest Color Variance Axis
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
ASCII Text
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Y. Sirisathitkul, S. Auwatanamongkol, B. Uyyanonvara, "Fast Color Image Quantization using Squared Euclidean Distance of Adjacent Color Points along the Highest Color Variance Axis," Pattern Recognition, International Conference on, vol. 1, pp. 656-659, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334251,
author = {Y. Sirisathitkul and S. Auwatanamongkol and B. Uyyanonvara},
title = {Fast Color Image Quantization using Squared Euclidean Distance of Adjacent Color Points along the Highest Color Variance Axis},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2004},
issn = {1051-4651},
pages = {656-659},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334251},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Fast Color Image Quantization using Squared Euclidean Distance of Adjacent Color Points along the Highest Color Variance Axis
SN - 1051-4651
SP656
EP659
A1 - Y. Sirisathitkul,
A1 - S. Auwatanamongkol,
A1 - B. Uyyanonvara,
PY - 2004
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Y. Sirisathitkul, National Institute of Development Administration, Bangkok, Thailand
S. Auwatanamongkol, National Institute of Development Administration, Bangkok, Thailand
B. Uyyanonvara, Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand
A new color image quantization algorithm that uses the squared Euclidean distance of adjacent color points along the highest color variance axis is proposed. This algorithm is a hierarchically divisive colormap design technique. Colors are sorted along the axis with the highest variance of color distribution. The squared Euclidean distances between any adjacent colors' along the axis are then used to find the cutting plane that divides a color cell into two subcells with approximately equal quantization errors respect to their centroids. The proposed algorithm is effective and yields a short execution time.
Citation:
Y. Sirisathitkul, S. Auwatanamongkol, B. Uyyanonvara, "Fast Color Image Quantization using Squared Euclidean Distance of Adjacent Color Points along the Highest Color Variance Axis," icpr, vol. 1, pp.656-659, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
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