Multilayer perceptron for rotationally invariant feature extraction and classification

Michael H. W. Smart; Alan F. Murray

doi:10.1117/12.235935

22 March 1996 Multilayer perceptron for rotationally invariant feature extraction and classification

Michael H. W. Smart, Alan F. Murray

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235935
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Michael H. W. Smart and Alan F. Murray "Multilayer perceptron for rotationally invariant feature extraction and classification", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235935

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