Competitive learning vector quantization with evolution strategies for image compression

2005 ◽  
Vol 44 (2) ◽  
pp. 027006 ◽  
Author(s):  
Ezzatollah Salari
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Competitive Learning ◽  
Evolution Strategies

Improved batch fuzzy learning vector quantization for image compression

2008 ◽  
Vol 178 (20) ◽  
pp. 3895-3907 ◽  
Author(s):  
George E. Tsekouras ◽  
Mamalis Antonios ◽  
Christos Anagnostopoulos ◽  
Damianos Gavalas ◽  
Dafne Economou
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Fuzzy Learning

scholarly journals Average Competitive Learning Vector Quantization

2013 ◽  
Vol 43 (6) ◽  
pp. 1288-1303
Author(s):  
Luis A. Salomón ◽  
Jean-Claude Fort ◽  
Li-Vang Lozada-Chang
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Competitive Learning

Competitive Learning with Fast Neuron-Insertion

2005 ◽  
Vol 9 (6) ◽  
pp. 590-598 ◽  
Author(s):  
Noritaka Shigei ◽  
◽  
Hiromi Miyajima ◽  
Michiharu Maeda ◽  
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Computational Cost ◽  
Competitive Learning ◽  
The Other ◽  
Computational Costs ◽  
Adaptive Vector Quantization ◽  
Small Set ◽  
Insertion Techniques ◽  
Conventional Methods

Adaptive Vector Quantization (AVQ) is to find a small set of weight vectors that well approximates a larger set of input vectors. This paper presents a fast AVQ method Competitive Learning with Approximate Neuron-Insertion (CLANI). Though neuron-insertion techniques can much enhance the accuracy in AVQ, a naive implementation requires a large computational cost proportional to the number of input vectors. Approximate neuron-insertion has an advantage that its computational cost is independent of the number of input vectors. We theoretically estimate the computational costs of CLANI and the other conventional methods. The effectiveness of CLANI is demonstrated in vector quantization simulations and an image compression application.

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