Multi-Scale Superpatch Matching using Dual Superpixel Descriptors
- URL: http://arxiv.org/abs/2003.04428v1
- Date: Mon, 9 Mar 2020 22:04:04 GMT
- Title: Multi-Scale Superpatch Matching using Dual Superpixel Descriptors
- Authors: R\'emi Giraud, Merlin Boyer, Micha\"el Cl\'ement
- Abstract summary: Over-segmentation into superpixels is a very effective dimensionality reduction strategy, enabling fast dense image processing.
The inherent irregularity of the image decomposition compared to standard hierarchical multi-resolution schemes is a problem.
We introduce the dual superpatch, a novel superpixel neighborhood descriptor.
- Score: 0.6875312133832078
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over-segmentation into superpixels is a very effective dimensionality
reduction strategy, enabling fast dense image processing. The main issue of
this approach is the inherent irregularity of the image decomposition compared
to standard hierarchical multi-resolution schemes, especially when searching
for similar neighboring patterns. Several works have attempted to overcome this
issue by taking into account the region irregularity into their comparison
model. Nevertheless, they remain sub-optimal to provide robust and accurate
superpixel neighborhood descriptors, since they only compute features within
each region, poorly capturing contour information at superpixel borders. In
this work, we address these limitations by introducing the dual superpatch, a
novel superpixel neighborhood descriptor. This structure contains features
computed in reduced superpixel regions, as well as at the interfaces of
multiple superpixels to explicitly capture contour structure information. A
fast multi-scale non-local matching framework is also introduced for the search
of similar descriptors at different resolution levels in an image dataset. The
proposed dual superpatch enables to more accurately capture similar structured
patterns at different scales, and we demonstrate the robustness and performance
of this new strategy on matching and supervised labeling applications.
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