We investigate the problem of learning optimal descriptors for a given
classification task. Many hand-crafted descriptors have been proposed
in the literature for measuring visual similarity. Looking past
initial differences, what really distinguishes one descriptor from
another is the trade-off that it achieves between discriminative power
and invariance. Since this trade-off must vary from task to task, no
single descriptor can be optimal in all situations.

Our focus, in this paper, is on learning the optimal trade-off for
classification given a particular training set and prior
constraints. The problem is posed in the kernel learning framework.
We learn the optimal, domain-specific kernel as a combination of base
kernels corresponding to base features which achieve different levels
of trade-off (such as no invariance, rotation invariance, scale
invariance, affine invariance, \etc) This leads to a convex
optimisation problem with a unique global optimum which can be solved
for efficiently. The method is shown to achieve state-of-the-art
performance on the UIUC textures, Oxford flowers and Caltech
101 datasets.