An Improved Learning Framework for Covariant Local Feature Detection
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 detec- tors 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 pro- pose 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 sta- ble features without using pre-determined features for training. Extensive experiments show our model achieves state-of-the-art performance in re- peatability score on the well known datasets such as Vgg-Affine, EF, and Webcam.
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