The matlab code used in the TIP 2013 paper ‘Rotation-Covariant Texture Learning Using Steerable Riesz Wavelets’ is now available. The code and the paper can be found in http://publications.hevs.ch/index.php/publications/show/1373
The paper is written by Adrien Depeursinge, Antonio Foncubierta-Rodriguez, Dimitri Van De Ville and Henning Müller.
Abstract—We propose a texture learning approach that exploits
local organizations of scales and directions. First, linear
combinations of Riesz wavelets are learned using kernel support
vector machines. The resulting texture “signatures” are modeling
optimal class–wise discriminatory properties. The visualization
of the obtained signatures allows verifying the visual relevance
of the learned concepts. Second, the local orientations of the
signatures are optimized to maximize their responses, which
is carried out analytically and can still be expressed as a
linear combination of the initial steerable Riesz templates. The
global process is iteratively repeated to obtain final rotation–
covariant texture signatures. Rapid convergence of class–wise
signatures is observed, which demonstrates that the instances
are projected into a feature space that leverages the local
organizations of scales and directions. Experimental evaluation
reveals an average classification accuracies in the range of 97%
to 98% for the Outex TC 00010, the Outex TC 00012, and the
Contrib TC 00000 suite for even orders of the Riesz transform,
and suggests high robustness to changes in images orientation
and illumination. The proposed framework requires no arbitrary
choices of scales and directions and is expected to perform well
in a large range of computer vision applications.