Title :
Image recognition systems with permutative coding
Author :
Kussul, E. ; Baidyk, T. ; Wunsch, D.C., II
Author_Institution :
Center of Appl. Sci. & Technol. Dev., National Autonomous Univ. of Mexico, Mexico
fDate :
31 July-4 Aug. 2005
Abstract :
A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%.
Keywords :
feature extraction; handwriting recognition; image coding; image recognition; neural nets; object recognition; visual databases; MNIST database; ORL database; face recognition; feature extractor; handwritten digit recognition; image recognition system; microobjects recognition; neural classifier; neural network; permutative coding; Error analysis; Face recognition; Feature extraction; Handwriting recognition; Image coding; Image databases; Image recognition; Neural networks; Shape; System testing;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556151
