Open AccessRootsGLOH2: embedding RootSIFT ‘square rooting’ in sGLOH2
Author(s) -
Bellavia Fabio,
Colombo Carlo
Publication year - 2020
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2019.0716
Subject(s) - embedding , scale invariant feature transform , artificial intelligence , matching (statistics) , pattern recognition (psychology) , robustness (evolution) , mathematics , image matching , euclidean geometry , square (algebra) , euclidean distance , planar , computer science , image (mathematics) , computer vision , geometry , statistics , biochemistry , chemistry , computer graphics (images) , gene
This study introduces an extension of the sGLOH2 local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state‐of‐the‐art non‐deep descriptors in recent evaluation contests dealing with both planar and non‐planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happens on SIFT, experimental evidence shows that classical norm‐based distances, such as the Euclidean and Manhattan distances, only provide suboptimal solutions to the problem of local image descriptor matching. This suggests matching distance design as a topic to investigate further in the near future.