DocumentCode
2855997
Title
Image recognition using fractal parameters
Author
Cha, Eui-Young ; Cho, Jae-Hyun ; Park, Chul-Woo ; Kim, Kwang-Baek
Author_Institution
Dept. of Comput. Sci., Pusan Nat. Univ., South Korea
Volume
3
fYear
1998
fDate
4-9 May 1998
Firstpage
1883
Abstract
Concerns the applications of fractal theory to image recognition and we propose the method that can enhance learning rate and recognition rate by using fractal parameters that are composed of input vectors for a neural network in an image recognition model. Fractal parameters with the properties of self-similarity and recursiveness can recover lossless original images through iterating processes. Therefore the original image can be implicitly represented and uniquely mapped by fractal parameters. The enhanced result is shown by computer simulations
Keywords
data compression; fractals; image classification; image coding; neural nets; object recognition; fractal parameters; fractal theory; image recognition; learning rate; recognition rate; recursiveness; self-similarity; Computer simulation; Electronic mail; Fractals; Humans; Image coding; Image recognition; Neural networks; Pattern recognition; Pixel; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.687145
Filename
687145
Link To Document