scholarly journals A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression. Appl. Sci. 2017, 7, 1106

2019 ◽  
Vol 9 (7) ◽  
pp. 1377
Author(s):  
Zunkai Huang ◽  
Dai Suzuki ◽  
Xiangyu Zhang ◽  
Lei Chen ◽  
Yongxin Zhu ◽  
...  
Keyword(s):  
Image Compression ◽  
High Speed ◽  
Self Organizing Map ◽  
Vector Quantizer ◽  
Efficient Vector ◽  
Published Paper ◽  
Self Organizing

We, the authors, wish to make the following corrections to our published paper [...]

scholarly journals A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

2017 ◽  
Vol 7 (11) ◽  
pp. 1106 ◽  
Author(s):  
Zunkai Huang ◽  
Xiangyu Zhang ◽  
Lei Chen ◽  
Yongxin Zhu ◽  
Fengwei An ◽  
...  
Keyword(s):  
Image Compression ◽  
High Speed ◽  
Self Organizing Map ◽  
Vector Quantizer ◽  
Efficient Vector ◽  
Self Organizing

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

A New Structured Self-Organizing Map with Dynamic Growth Applied to Image Compression

2016 ◽  
Author(s):  
José Alfredo Ferreira Costa ◽  
A. Duarte D. Neto ◽  
Márcio Luiz A. Netto
Keyword(s):  
Image Compression ◽  
Self Organizing Map ◽  
Dynamic Growth ◽  
Self Organizing
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