Paper Info
Title
Hierarchical Color Quantization Based on Self-organization
Abstract
In this paper, a new hierarchical color quantization method based on self-organizing maps that provides different levels of quantization is presented. Color quantization (CQ) is a typical image processing task, which consists of selecting a small number of code vectors from a set of available colors to represent a high color resolution image with minimum perceptual distortion. Several techniques have been proposed for CQ based on splitting algorithms or cluster analysis. Artificial neural networks and, more concretely, self-organizing models have been usually utilized for this purpose. The self-organizing map (SOM) is one of the most useful algorithms for color image quantization. However, it has some difficulties related to its fixed network architecture and the lack of representation of hierarchical relationships among data. The growing hierarchical SOM (GHSOM) tries to face these problems derived from the SOM model. The architecture of the GHSOM is established during the unsupervised learning process according to the input data. Furthermore, the proposed color quantizer allows the evaluation of different color quantization rates under different codebook sizes, according to the number of levels of the generated neural hierarchy. The experimental results show the good performance of this approach compared to other quantizers based on self-organization.
Year
DOI
Venue
2014
10.1007/s10851-013-0433-8
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
Color quantization,Hierarchical compression,Self-organization,GHSOM
Computer vision,High color,Image processing,Unsupervised learning,Vector quantization,Artificial intelligence,Quantization (signal processing),Artificial neural network,Mathematics,Color quantization,Codebook
Journal
Volume
Issue
ISSN
49
1
0924-9907
Citations 
PageRank 
References 
12
0.57
19
Authors
2
Name
Order
Citations
PageRank
Esteban J. Palomo19514.79
Enrique Domínguez213321.24