TailoredBRIEF: Online per-feature descriptor customization

Abstract

Image feature descriptors composed of a series of binary intensity comparisons yield substantial memory and runtime improvements over conventional descriptors, but are sensitive to viewpoint changes in ways that vary per feature. We propose a method to improve the matching performance of such descriptors by specifically reasoning about the reliability of test results on a feature-by-feature basis. We demonstrate an intuitive method to learn improved descriptor structures for individual features. Further, these learned results can be efficiently applied during matching with little increase in runtime. We provide an evaluation using a standard, ground-truthed, multi-image dataset.