A new feasible approach to multi-dimensional scale saliency

Abstract

In this paper, we present a multi-dimensional extension of an image feature extractor, the scale saliency algorithm by Kadir and Brady. In order to avoid the curse of dimensionality, our algorithm is based on a recent Shannon's entropy estimator and on a new divergence metric in the spirit of Friedman's and Rafsky estimation of Henze-Penrose divergence. The experiments show that, compared to our previous existing method based on entropic graphs, this approach remarkably decreases computation time, while not significantly deterioring the quality of the results. © 2009 Springer Berlin Heidelberg.