A maximum diversity-based path sparsification for geometric graph matching
| Autor: | Karima Amrouche, Abd Errahmane Kiouche, Hamida Seba |
|---|---|
| Přispěvatelé: | Graphes, AlgOrithmes et AppLications (GOAL), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), École Nationale Supérieure d'Informatique [Alger] (ESI), ANR-20-CE23-0002,COREGRAPHIE,COmpression de REseaux et de GRAPHes pour une Informatique Efficace(2020) |
| Rok vydání: | 2021 |
| Předmět: |
Matching (graph theory)
Computer science [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] Shape matching Similarity measure 01 natural sciences Measure (mathematics) 010305 fluids & plasmas Image (mathematics) Spatial network Artificial Intelligence 0103 physical sciences Graph matching 010306 general physics Graph sparsification [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Geometric graphs Signal Processing Path (graph theory) Embedding Node (circuits) Computer Vision and Pattern Recognition Algorithm Maximum diversity problem Software MathematicsofComputing_DISCRETEMATHEMATICS |
| Zdroj: | Pattern Recognition Letters Pattern Recognition Letters, Elsevier, 2021, 152, pp.107-114. ⟨10.1016/j.patrec.2021.09.019⟩ |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2021.09.019 |
| Popis: | This paper presents an effective dissimilarity measure for geometric graphs representing shapes. The proposed dissimilarity measure is a distance that combines a sparsification of the geometric graph based on the maximum diversity problem and a new node embedding that captures the topological neighborhood of nodes. The sparsification step aims to reduce the size of the graph and to correct the misdistribution of nodes on the geometric graph induced by the noise of image handling. Experimental evaluation shows that the sparsification algorithm retains the form of the shapes while decreasing the number of processed nodes which reduces the overall matching time. Furthermore, the proposed node embedding and similarity measure give better performance in comparison with existing graph matching approaches. |
| Databáze: | OpenAIRE |
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