Multi-scale superpatch matching using dual superpixel descriptors
dc.rights.license | open | en_US |
hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | GIRAUD, Remi
IDREF: 22338545 | |
hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
dc.contributor.author | BOYER, Merlin | |
hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
dc.contributor.author | CLEMENT, Michael
IDREF: 22471175X | |
dc.date.accessioned | 2022-11-22T08:59:57Z | |
dc.date.available | 2022-11-22T08:59:57Z | |
dc.date.issued | 2020-05-01 | |
dc.identifier.issn | 0167-8655 | en_US |
dc.identifier.uri | oai:crossref.org:10.48550/arxiv.2003.04428 | |
dc.identifier.uri | oai:crossref.org:10.1016/j.patrec.2020.02.018 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/170340 | |
dc.description.abstractEn | 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. | |
dc.language.iso | EN | en_US |
dc.source | crossref | |
dc.title.en | Multi-scale superpatch matching using dual superpixel descriptors | |
dc.type | Article de revue | en_US |
dc.identifier.doi | 10.1016/j.patrec.2020.02.018 | en_US |
dc.subject.hal | Informatique [cs] | en_US |
dc.identifier.arxiv | arXiv:2003.04428 | en_US |
bordeaux.journal | Pattern Recognition Letters | en_US |
bordeaux.page | 129-136 | en_US |
bordeaux.volume | 133 | en_US |
bordeaux.hal.laboratories | IMS : Laboratoire d’Intégration du Matériau au Système - UMR 5218 | en_US |
bordeaux.institution | Université de Bordeaux | en_US |
bordeaux.institution | Bordeaux INP | en_US |
bordeaux.institution | CNRS | en_US |
bordeaux.peerReviewed | oui | en_US |
bordeaux.inpress | non | en_US |
bordeaux.import.source | dissemin | |
hal.export | false | |
workflow.import.source | dissemin | |
dc.rights.cc | Pas de Licence CC | en_US |
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