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Dimension Reduction and Classification Methods for Object Recognition in Vision

Charles Bouveyron 1 Stephane Girard 1 Cordelia Schmid 2, *
* Corresponding author
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : 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. We will first describe the two stages of the recognition process (See Fig. 1), learning and recognition, then we will present experimental results obtained on motorbikes images.
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Charles Bouveyron, Stephane Girard, Cordelia Schmid. Dimension Reduction and Classification Methods for Object Recognition in Vision. 5th French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, May 2004, Saint-Pierre de Chartreuse, France. pp.109--113. ⟨inria-00548547⟩

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