Abstract
A novel algebraic feature extraction method for image recognition is presented. The main difference between the present method and any other existing algebraic feature extraction method is that the present method uses an optimal discriminant criterion to extract algebraic features. For the given training image samples, a set of optimal discriminant projection vectors is calculated according to a generalized Fisher criterion function. On the basis of the optimal discriminant projection vector, the projective feature vectors of an image (i.e. algebraic features) are extracted by projecting the image onto all optimal discriminant projection vectors. The method of calculating the set of optimal discriminant projection vectors is discussed. A minimum distance classifier is also proposed for classifying projective feature vectors. Experimental results showed that the present method has good recognition performance. An important conclusion about the present method is that the Foley-Sammon optimal set of discriminant vectors is a special case of the set of optimal discriminant projection vectors.
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