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Digit Recognition Based on Distance Metric by Gabor Transformation

Distance metric is important for image transform, it reflects the similarity of manifold between pre and post transformation. Tangent distance method is usually used to approximate nonlinear manifolds by its tangent hyperplane in digit recognition; however, it sometimes neglects the high-order statistic information of the image. A new image distance metric is proposed in this paper which is based on Gabor transformation to obtain the Gabor feature vector, then the feature manifold can be approximated by curve surfaces based on second-order Taylor expansion with respect to intrinsic variables, and the distance metric by Gabor transformation is defined as the minimum distance between the approximated curved surfaces in feature observation space. Experiments show that the distance metric based on Gabor transformation and manifold works well on digit recognition, it excelled tangent distance method.