Optimized Nonlinear Discriminant Analysis (ONDA) for Supervised Pixel Classification

Filter bank-based methods for pixel classification are attractive due to the potential of fast implementation with convolution operations. The design of optimal filter sets, however, is a challenging task given the nonlinear aspects of the problem. This letter extends the well known linear discriminant analysis method into a novel framework for local texture feature discrimination tasks. It proposes a mixture of linear models as a nonlinear classifier, where a number of filters are optimized locally by minimizing the prediction error. Through these filters, the `best separable' features are selected. Experiments performed on two standard texture databases show that our method produces results which are comparable to state-of-the-art techniques while at the same time maintaining low computational complexity.