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Abstract : |
Abstract. Statistical classication using tangent vectors and classi-cation based on local features are two successful methods for various image recognition problems. These two approaches tolerate global and local transformations of the images, respectively. Tangent vectors can be used to obtain global invariance with respect to small ane transformations and line thickness, for example. On the other hand, a classier based on local representations admits the distortion of parts of the image. From these properties, a combination of the two approaches seems very likely to improve on the results of the individual approaches. In this paper we show the benets of this combination by applying it to the well known USPS handwritten digits recognition task. An error rate of 2.0% is obtained, which is the best result published so far on this dataset. 1, |
