Quantization vs Organization in the Kohonen S.O.M

Low bitrate image compression using self-organized Kohonen maps

In this paper, we propose a new image compression algorithm based on Kohonen self-organized maps. The compression is based on vector quantization (VQ) of the DCT coefficients of image blocks, where the VQ is implemented by a Kohonen network. At low bitrates, our proposed method performs better than an earlier compression scheme developed by Amerijckx et al. [1] and shows better subjective resul...

Self-organization of a Kohonen network with quantized weights and a arbitrary one-dimensional stimuli distribution

Long-Term Time Series Forecasting Using Self-Organizing Maps: the Double Vector Quantization Method

Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for long-term trends prediction, with a double application of the Kohonen algorithm. We also consider practical issues for the use of the method.

Kohonen Networks and Clustering

The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -k-means ...

Kohonen Networks and Clustering: Comparative Performance in Color Clustering

The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -k-means ...