Color Quantization with Magnitude Sensitive Competitive Learning Algorithm

2014 ◽  
pp. 212-231 ◽  
Author(s):  
Enrique Pelayo ◽  
David Buldain ◽  
Carlos Orrite
Keyword(s):  
Learning Algorithm ◽  
Competitive Learning ◽  
Color Quantization

Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning

2010 ◽  
Vol 14 (4) ◽  
pp. 375-381
Author(s):  
Jun Zhang ◽  
◽  
Jinglu Hu
Keyword(s):  
Binary Tree ◽  
Competitive Learning ◽  
Tree Structure ◽  
Experimental Results ◽  
Color Quantization ◽  
Adaptive Procedure ◽  
Root Node ◽  
Binary Tree Structure

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.

Reduction Models in Competitive Learning Founded on Distortion Standards

2008 ◽  
Vol 12 (3) ◽  
pp. 314-323 ◽  
Author(s):  
Michiharu Maeda ◽  
◽  
Noritaka Shigei ◽  
Hiromi Miyajima ◽  
Kenichi Suzaki ◽  
...  
Keyword(s):  
Image Coding ◽  
Learning Algorithm ◽  
Computation Time ◽  
Competitive Learning ◽  
Experimental Results ◽  
Error Criterion ◽  
General Reduction ◽  
Predetermined Number

Two reductions in competitive learning founded on distortion standards are discussed from the viewpoint of generating necessary and appropriate reference vectors under the condition of their predetermined number. The first approach is termed the segmental reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially eliminated to reach their prespecified number based on the partition error criterion. The second approach is termed the general reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially erased based on the average distortion criterion. Experimental results demonstrate the effectiveness of our approaches compared to conventional techniques in average distortion. The two approaches are applied to image coding to determine their feasibility in quality and computation time.

Partial-distortion-weighted fuzzy competitive learning algorithm for vector quantisation

1994 ◽  
Vol 30 (6) ◽  
pp. 505-506 ◽  
Author(s):  
C. Zhu ◽  
L.H. Li ◽  
Z.Y. He ◽  
T.J. Wang
Keyword(s):  
Learning Algorithm ◽  
Competitive Learning ◽  
Vector Quantisation ◽  
Partial Distortion
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