Automatic configuration of spectral dimensionality reduction methods for 3D human pose estimation

In this paper, our main contribution is a framework for the automatic configuration of any spectral dimensionality reduction methods. This is achieved, first, by introducing the mutual information measure to assess the quality of discovered embedded spaces. Secondly, we overcome the deficiency of mapping function in spectral dimensionality reduction approaches by proposing data projection between spaces based on fully automatic and dynamically adjustable Radial Basis Function network. Finally, this automatic framework is evaluated in the context of 3D human pose estimation. We demonstrate mutual information measure outperforms all current space assessment metrics. Moreover, experiments show the mapping associated to the induced embedded space displays good generalization properties. In particular, it allows improvement of accuracy by around 30% when refining 3D pose estimates of a walking sequence produced by an activity independent method.