Title :
Utilizing the similarity preserving properties of self-organizing maps in vector quantization of images
Author_Institution :
Neural Networks Res. Centre, Helsinki Univ. of Technol., Espoo, Finland
Abstract :
The self-organizing map (SOM) algorithm creates a topologically ordered mapping from the input space to map nodes. The mapping has the special property that the neighborhood relations between the input samples are preserved to the output space. In this paper it is shown that the similarity preserving property of the SOM can be used advantageously in image vector quantization applications, either to increase the error tolerance for transmission errors, or to increase the compression efficiency
Keywords :
image coding; self-organising feature maps; vector quantisation; VQ; compression efficiency; error tolerance; image vector quantization; neighborhood relations; self-organizing maps; similarity-preserving properties; topologically ordered mapping; transmission errors; Clustering algorithms; Data analysis; Image coding; Intelligent networks; Iterative algorithms; Neural networks; Pattern recognition; Self organizing feature maps; Space technology; Vector quantization;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488996
