A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%
E. M. Kussul et al., "Permutation Coding Technique for Image Recognition Systems," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/TNN.2006.880676
Electrical and Computer Engineering
Keywords and Phrases
MNIST Database; Olivetti Research Laboratory (ORL) Database; Face Recognition; Feature Extraction; Handwritten Digit Recognition; Image Coding; Image Recognition; Image Recognition Systems; Neural Classifier; Neural Nets; Permutation Coding Neural Classifier; Permutation Coding Technique; Random Local Descriptors
International Standard Serial Number (ISSN)
Article - Journal
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