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Kernel estimation techniques, such as mean shift, suffer from one majordrawback: the kernel bandwidth selection. The bandwidth can be fixed for allthe data set or can vary at each points. Automatic bandwidth selection becomesa real challenge in case of multidimensional heterogeneous features. This paperpresents a solution to this problem. It is an extension of \cite{Comaniciu03a}which was based on the fundamental property of normal distributions regardingthe bias of the normalized density gradient. The selection is done iterativelyfor each type of features, by looking for the stability of local bandwidthestimates across a predefined range of bandwidths. A pseudo balloon mean shiftfiltering and partitioning are introduced. The validity of the method isdemonstrated in the context of color image segmentation based on a5-dimensional space.

Bandwidth selection for kernel estimation in mixed multi-dimensional spaces