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Reports (Research Report) Year : 2007

Bandwidth selection for kernel estimation in mixed multi-dimensional spaces

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Aurelie Bugeau
Patrick Pérez
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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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Dates and versions

inria-00171686 , version 1 (12-09-2007)
inria-00171686 , version 2 (14-09-2007)

Identifiers

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

Cite

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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