SIFT Matching by Context Exposed

TL;DR

This paper introduces novel context-based strategies for local image descriptor matching, including blob matching and Delaunay Triangulation Matching, improving accuracy and robustness over existing methods.

Contribution

It proposes new matching and filtering techniques that leverage contextual information, enhancing local descriptor matching performance.

Findings

Delaunay Triangulation Matching (DTM) outperforms or matches state-of-the-art methods.

Blob matching improves matching accuracy through a flexible framework.

The new benchmark effectively evaluates matching pipeline robustness.

Abstract

This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetric matching, enabling to achieve a global improvement upon each individual strategy. DTM alternates between Delaunay triangulation contractions and expansions to figure out and adjust keypoint neighborhood consistency. Experimental evaluation shows that DTM is comparable or better than the state-of-the-art in terms of matching accuracy and robustness. Evaluation is carried out according to a new benchmark devised for analyzing the matching pipeline in terms of correct correspondences on both planar and non-planar scenes, including several state-of-the-art methods as well as the common SIFT matching approach for reference. This evaluation can be of assistance for future research in this field.