GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Clustering in high-dimensional spaces is a recurrent problem in many domains, for example in object recognition. High-dimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.
https://hal.inria.fr/inria-00548591 Contributor : THOTH TeamConnect in order to contact the contributor Submitted on : Thursday, January 6, 2011 - 9:21:10 AM Last modification on : Thursday, January 20, 2022 - 5:30:13 PM Long-term archiving on: : Thursday, April 7, 2011 - 2:30:38 AM
Charles Bouveyron, Stéphane Girard, Cordelia Schmid. High dimensional data clustering. 17th International Conference on Computational Statistics (Compstat '06), Aug 2006, Rome, Italy. pp.813--820. ⟨inria-00548591⟩