PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 [4] showed that the SIFT descriptor [2] 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:836
Deposited By:Charles Bouveyron
Deposited On:01 January 2005