Clustering with K-Harmonic Means Applied to Colour Image Quantization

被引：6

作者：

Frackiewicz, Mariusz ^{[1
]}

Palus, Henryk ^{[1
]}

机构：

[1] Silesian Tech Univ, Inst Automat Control, PL-44100 Gliwice, Poland

来源：

ISSPIT: 8TH IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY | 2008年

关键词：

colour image quantization; clustering; k-means; k-harmonic means; quality measures;

D O I：

10.1109/ISSPIT.2008.4775684

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The main goal of colour quantization methods is a colour reduction with minimum colour error. In this paper were investigated six following colour quantization techniques: the classical median cut, improved median cut, clustering k-means technique in two colour versions (RGB, CIELAB) and also two versions of relative novel technique named k-harmonic means. The comparison presented here was based on testing of ten natural colour images for quantization into 16, 64 and 256 colours. In evaluation process two criteria were used: the mean squared quantization error (MSE) and the average error in the CIELAB colour space (Delta E). During tests the efficiency of k-harmonic means applied to colour quantization has been proved.

引用

页码：52 / 57

页数：6

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