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Bandwidth selection for kernel estimation in mixed multi-dimensional spaces

Aurelie Bugeau 1 Patrick Pérez 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. The bandwidth can be fixed for all the data set or can vary at each points. Automatic bandwidth selection becomes a real challenge in case of multidimensional heterogeneous features. This paper presents a solution to this problem. It is an extension of \cite{Comaniciu03a} which was based on the fundamental property of normal distributions regarding the bias of the normalized density gradient. The selection is done iteratively for each type of features, by looking for the stability of local bandwidth estimates across a predefined range of bandwidths. A pseudo balloon mean shift filtering and partitioning are introduced. The validity of the method is demonstrated in the context of color image segmentation based on a 5-dimensional space.
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Submitted on : Friday, September 14, 2007 - 10:34:56 AM
Last modification on : Wednesday, March 24, 2021 - 9:44:04 AM
Long-term archiving on: : Tuesday, September 21, 2010 - 1:25:28 PM


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  • HAL Id : inria-00171686, version 2
  • ARXIV : 0709.1920


Aurelie Bugeau, Patrick Pérez. Bandwidth selection for kernel estimation in mixed multi-dimensional spaces. [Research Report] RR-6286, INRIA. 2007. ⟨inria-00171686v2⟩



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