  Learning feature detection has been largely an unexplored area when compared
to handcrafted feature detection. Recent learning formulations use the
covariant constraint in their loss function to learn covariant detectors.
However, just learning from covariant constraint can lead to detection of
unstable features. To impart further, stability detectors are trained to
extract pre-determined features obtained by hand-crafted detectors. However, in
the process they lose the ability to detect novel features. In an attempt to
overcome the above limitations, we propose an improved scheme by incorporating
covariant constraints in form of triplets with addition to an affine covariant
constraint. We show that using these additional constraints one can learn to
detect novel and stable features without using pre-determined features for
training. Extensive experiments show our model achieves state-of-the-art
performance in repeatability score on the well known datasets such as
Vgg-Affine, EF, and Webcam.
