Wavelet Based Recognition Using Model Theory for Feature Selection

An increase in accuracy and reduction in computational complexity of the common wavelet-based target recognition techniques can be achieved by using interpretable features for recognition. In this work, the Best Discrimination Basis Algorithm (BDBA) is applied to select the most discriminant complete orthonormal wavelet basis for recognition purposes. The BDBA uses a relative entropy criterion as a discriminant measure. Then, interpretable features are selected from the most discriminant basis by utilizing symbolic knowledge about the domain. The domain theory that contains this symbolic knowledge is implemented in a backpropagation neural network. The output of the backpropagation neural network gives a nal recognition decision. The results of our simulations show that the recognition accuracy of the proposed Automatic Feature Based Recognition System (AFBRS) is better than the recognition accuracy of a system that performs recognition using the Most Discriminant Wavelet Coeecients (MDWC).