Title :
Unsupervised learning applied to image coding
Author :
Xu, Meina ; Kuh, Anthony
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI, USA
fDate :
30 Apr-3 May 1995
Abstract :
We examine the performance of several neural network vector quantization (VQ) methods on image coding. The VQ methods we look at are Kohonen´s Self-Organizing Feature Map (KSOFM), Frequency-Sensitive Competitive Learning (FSCL), and Self-Creating and Organizing Neural Network (SCONN). We also look at variations of these algorithms by combining different methods. Our simulation results show that the best performance is achieved by the SCONN and the combination KSOFM and FSCL
Keywords :
image coding; self-organising feature maps; unsupervised learning; vector quantisation; FSCL; Frequency-Sensitive Competitive Learning; KSOFM; Kohonen Self-Organizing Feature Map; SCONN; Self-Creating and Organizing Neural Network; algorithms; image coding; neural network; simulation; unsupervised learning; vector quantization; Clustering algorithms; Counting circuits; Euclidean distance; Frequency; Image coding; Neural networks; Neurons; Pixel; Power capacitors; Unsupervised learning;
Conference_Titel :
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2570-2
DOI :
10.1109/ISCAS.1995.523722
