  Feature detection is an important procedure for image matching, where
unsupervised feature detection methods are the detection approaches that have
been mostly studied recently, including the ones that are based on
repeatability requirement to define loss functions, and the ones that attempt
to use descriptor matching to drive the optimization of the pipelines. For the
former type, mean square error (MSE) is usually used which cannot provide
strong constraint for training and can make the model easy to be stuck into the
collapsed solution. For the later one, due to the down sampling operation and
the expansion of receptive fields, the details can be lost for local
descriptors can be lost, making the constraint not fine enough. Considering the
issues above, we propose to combine both ideas, which including three aspects.
1) We propose to achieve fine constraint based on the requirement of
repeatability while coarse constraint with guidance of deep convolution
features. 2) To address the issue that optimization with MSE is limited,
entropy-based cost function is utilized, both soft cross-entropy and
self-information. 3) With the guidance of convolution features, we define the
cost function from both positive and negative sides. Finally, we study the
effect of each modification proposed and experiments demonstrate that our
method achieves competitive results over the state-of-the-art approaches.
