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%.
E. M. Kussul et al., "Image Recognition Systems with Permutative Coding," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/IJCNN.2005.1556151
IEEE International Joint Conference on Neural Networks, 2005
Electrical and Computer Engineering
Keywords and Phrases
MNIST Database; ORL Database; Face Recognition; Feature Extraction; Feature Extractor; Handwriting Recognition; Handwritten Digit Recognition; Image Coding; Image Recognition; Image Recognition System; Microobjects Recognition; Neural Classifier; Neural Nets; Neural Network; Object Recognition; Permutative Coding; Visual Databases
Article - Conference proceedings
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2005