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Recognition of Superimposed Patterns with Selective Attention based on SVM  

Bae, Kyu-Chan (Department of Electrical Engineering & Computer Science and Brain Science Research Center, Korea Advanced Institute of Science and Technology)
Park, Hyung-Min (Department of Electrical Engineering & Computer Science and Brain Science Research Center, Korea Advanced Institute of Science and Technology)
Oh, Sang-Hoon (Division of Information Communivation and Radio Engineering, Mokwon University)
Choi, Youg-Sun (Department of Biosystems and Brain Science Research Center, Korea Advanced Institute of Science and Technology)
Lee, Soo-Young (Department of Biosystems and Brain Science Research Center, Korea Advanced Institute of Science and Technology)
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Abstract
We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.
Keywords
SVM;
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