Multilayer perceptron for rotationally invariant feature extraction and classification
Michael H. W. Smart, Alan F. Murray · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
In this paper we introduce a technique for incorporating adaptive, rotationally invariant (RI), feature extraction into the initial layer parameters of a multilayer perceptron for classifying real IR imagery. Feature extraction parameters are not usually estimated directly due to their high dimensionality but it is possible to reduce the dimensionality by constraining these parameters to a feature subspace where the parameters are restricted to a continuous RI generating functional form (e.g. a circularly symmetric radial polynomial transform.) The lower dimensional function parameters and the classification parameters can then be estimated simultaneously to minimize an overall classification error criterion. This can be considered as an extension of previous work by other authors where non-RI filter parameters, such as Gabor filter directional selectivity, were successfully tuned for feature extraction.