25 October 1988 Vector Quantization Of Images Based Upon A Neural-Network Clustering Algorithm
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Proceedings Volume 1001, Visual Communications and Image Processing '88: Third in a Series; (1988) https://doi.org/10.1117/12.968954
Event: Visual Communications and Image Processing III, 1988, Cambridge, MA, United States
Abstract
A neural-network clustering algorithm proposed by Kohonen is used to design a codebook for the vector quantization of images. This neural-network clustering algorithm, which is better known as the Kohonen Self-Organizing Feature Maps is a two-dimensional extensively interconnected nodes or unit of processors. The synaptic strengths between the input and the output nodes represent the centroid of the clusters after the network has been adapted to the input vector patterns. Input vectors are presented one at a time, and the weights connecting the input signals to the neurons are adaptively updated such that the point density function of the weights tend to approximate the probability density function of the input vector. Results are presented for a number of coded images using the codebook designed by the Self-Organization Feature Maps. The results are compared with coded images when the codebook is designed by the well known Linde-Buzo-Gray (LBG) algorithm.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nasser M. Nasrabadi, Nasser M. Nasrabadi, Yushu Feng, Yushu Feng, } "Vector Quantization Of Images Based Upon A Neural-Network Clustering Algorithm", Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.968954; https://doi.org/10.1117/12.968954
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