Title :
Landscape image analysis using fuzzy adaptive resonance theory
Author :
Chen, Jia-Lin ; Chang, Jyh- Yeong ; Tsay, Ming-Lay
Author_Institution :
Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
6/21/1905 12:00:00 AM
Abstract :
Describes an image analysis system using fuzzy methodology. Some landscapes, which are composed of natural things, are chosen as target images. In this system, the fuzzy adaptive resonance theory (fuzzy ART) algorithm is first used to cluster the constituent elements of the landscape images. The means and variance of the clusters are computed and then used to determine the membership functions of fuzzy sets. Based on the derived membership functions, a supervised learning algorithm is used to generate fuzzy rules automatically, and classification will then be facilitated through fuzzy max-min inference. Simulation on real pictures of scenery has shown that the proposed image analyzer is very successful because the result is visually confirmed by human observation
Keywords :
adaptive resonance theory; fuzzy logic; fuzzy set theory; image classification; learning (artificial intelligence); pattern clustering; classification; clusters; fuzzy adaptive resonance theory; fuzzy max-min inference; fuzzy rules; human observation; landscape image analysis; membership functions; scenery; supervised learning algorithm; Adaptive systems; Clustering algorithms; Computational modeling; Fuzzy sets; Fuzzy systems; Image analysis; Inference algorithms; Resonance; Subspace constraints; Supervised learning;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.823159
