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Local covariant feature detection, namely the problem of extracting viewpointinvariant features from images, has so far largely resisted the application ofmachine learning techniques. In this paper, we propose the first fully generalformulation for learning local covariant feature detectors. We propose to castdetection as a regression problem, enabling the use of powerful regressors suchas deep neural networks. We then derive a covariance constraint that can beused to automatically learn which visual structures provide stable anchors forlocal feature detection. We support these ideas theoretically, proposing anovel analysis of local features in term of geometric transformations, and weshow that all common and many uncommon detectors can be derived in thisframework. Finally, we present empirical results on translation and rotationcovariant detectors on standard feature benchmarks, showing the power andflexibility of the framework.

Learning Covariant Feature Detectors