Abstract
In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.
| Original language | English |
|---|---|
| Pages (from-to) | 375-381 |
| Number of pages | 7 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 14 |
| Issue number | 4 |
| Publication status | Published - 2010 May |
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Keywords
- Color Quantization (CQ)
- Splitmerging conditions
- Tree structure
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
Cite this
Color Quantization based on Hierarchical Frequency Sensitive Competitive Learning. / Zhang, Jun; Furuzuki, Takayuki.
In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 4, 05.2010, p. 375-381.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Color Quantization based on Hierarchical Frequency Sensitive Competitive Learning
AU - Zhang, Jun
AU - Furuzuki, Takayuki
PY - 2010/5
Y1 - 2010/5
N2 - In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.
AB - In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.
KW - Color Quantization (CQ)
KW - Splitmerging conditions
KW - Tree structure
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UR - http://www.scopus.com/inward/citedby.url?scp=77952712266&partnerID=8YFLogxK
M3 - Article
VL - 14
SP - 375
EP - 381
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
SN - 1343-0130
IS - 4
ER -