A learning based feature point detector
Alexander Verichev · 2017
We propose a learning-based image feature points detector. Instead of giving an explicit definition for feature point we apply the methods of machine learning to infer it inductively using a representative training set. This allows for a flexible tuning of the proposed detector to a specific problem that is described by a training set of desired responses. To increase feature points' repeatability and robustness to various image transformations the feature space of the learning algorithm includes raw image moments and image moment invariants. Experiments demonstrate high flexibility in tuning the detector to a specific task, acceptable repeatability of the feature points and robustness to various image transformations.