This study introduces an extension of the shifting gradient local orientation histogram doubled (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 scale invariant feature transform, 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.

Fabio Bellavia, Carlo Colombo (2020). RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2. IET COMPUTER VISION, 14(4), 138-143 [10.1049/iet-cvi.2019.0716].

RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2

Fabio Bellavia;
2020-01-01

Abstract

This study introduces an extension of the shifting gradient local orientation histogram doubled (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 scale invariant feature transform, 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.
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Fabio Bellavia, Carlo Colombo (2020). RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2. IET COMPUTER VISION, 14(4), 138-143 [10.1049/iet-cvi.2019.0716].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/403134
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