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
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;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687145
