WαSH: Weighted Α-Shapes for Local Feature Detection

Abstract

Depending on the application, local feature detectors should comply with properties that are often contradictory, e.g. distinctiveness vs. robustness. Providing a good balance is a standing problem in the field. In this direction, we propose a novel approach for local feature detection starting from sampled edges. The detector is based on shape stability measures across the weighted α-filtration , a computational geometry construction that captures the shape of a non-uniform set of points. The extracted features are blob-like and include non-extremal regions as well as regions determined by cavities of boundary shape. Overall, the approach provides distinctive regions, while achieving high robustness in terms of repeatability and matching score , as well as competitive performance in a large scale image retrieval application.