Skip to Main content Skip to Navigation
Conference papers

Dimension Reduction and Classification Methods for Object Recognition in Vision

Charles Bouveyron 1 Stéphane 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [5 references]  Display  Hide  Download
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 9:09:35 AM
Last modification on : Thursday, January 20, 2022 - 5:30:12 PM
Long-term archiving on: : Monday, March 21, 2011 - 3:14:49 AM


Files produced by the author(s)


  • HAL Id : inria-00548547, version 1



Charles Bouveyron, Stéphane 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⟩



Record views


Files downloads