Dimension Reduction and Classification Methods for Object Recognition in Vision
Charles Bouveyron, Cordelia Schmid and Stéphane Girard
In: 5th French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, 10-13 may 2004, Saint-pierre de Chartreuse - France.
This paper addresses the challenging task of recognizing and locating objects in natural images. In computer vision, many successful approaches to object recognition use local image descriptors. Such descriptors do not require segmentation, in addition they are robust to partial occlusion and invariant to image transformations (particularly scale changes). Among the existing descriptors, a recent comparison  showed that the SIFT descriptor  was particularly robust. However, the SIFT descriptor is high-dimensional (typically 128-dimensional) and this penalizes classification. In this paper, we propose to use statistical dimension reduction techniques to obtain a more discriminant representation of data, in order to increase recognition results.