Title :
A self-organizing neural network for image segmentation
Author :
Kong, H. ; Guan, L.
Author_Institution :
Dept. of Electr. Eng., Sydney Univ., NSW, Australia
fDate :
29 Nov-2 Dec 1994
Abstract :
A new method is proposed for multiscale image segmentation. The method is based on pixel classification by means of a self organizing neural network. The core concept of this processing method is to explicitly treat segmentation as a classification problem. An unsupervised learning algorithm is utilized in the processing. Compared with other segmentation methods, the proposed one has a number of desirable features. It is self adaptive, efficient, and easy to control. The effectiveness of the proposed method is verified through several experiments
Keywords :
image classification; image segmentation; self-organising feature maps; unsupervised learning; classification problem; image segmentation; multiscale image segmentation; pixel classification; self organizing neural network; self-organizing neural network; unsupervised learning algorithm; Automatic control; Image color analysis; Image resolution; Image segmentation; Image texture analysis; Neural networks; Supervised learning; Unsupervised learning; Visualization; Volume measurement;
Conference_Titel :
Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-7803-2404-8
DOI :
10.1109/ANZIIS.1994.396956
