Optimal waveform representation of shape and texture features for image classification
Arturo S. Dimalanta, Keith Phillips · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem and improve classification accuracy. We further enhance classification performance by finding an optimal representation of the extracted image features which maximizes separability distance among classes. The principal tools used are Fourier series, wavelet packets, local discriminant basis analysis, and neural networks.