Seeking affinity structure: Strategies for improving m-best graph matching
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http://hdl.handle.net/10045/96433
Título: | Seeking affinity structure: Strategies for improving m-best graph matching |
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Autor/es: | Curado, Manuel | Escolano, Francisco | Lozano, Miguel Angel | Hancock, Edwin R. |
Grupo/s de investigación o GITE: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | m-best graph matching | Binary-tree partitions | QAP |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | ene-2020 |
Editor: | Elsevier |
Cita bibliográfica: | Information Sciences. 2020, 509: 164-182. doi:10.1016/j.ins.2019.09.014 |
Resumen: | State-of-the-art methods for finding the m-best solutions to graph matching (QAP) rely on exclusion strategies. The k-th best solution is found by excluding all better ones from the search space. This provides diversity, a natural requirement for transforming a MAP problem into a m-best one. Since diversity enforces mode hopping, it is usually combined with a mode-approximation strategy such as marginalisation. However, these methods are generic insofar they do not incorporate the detailed structure of the problem at hand, i.e. the properties of the global affinity matrix which characterise the search space. Without this knowledge, it is thus hard to devise a practical criterion for choosing the next variable to clamp. In this paper, we propose several strategies to select the next variable to clamp, spanning the whole range between depth-first and breadth-first search, and we contribute with a unifying view for characterising the search space on the fly. Our strategies are: a) Number of factors in which the variables participate, b) centrality measures associated with the affinity matrix, and c) discrete pooling. Our experiments show that max number of factors and centrality provide a trade-off between efficiency and accuracy, whereas discrete pooling leads to an improvement of the state-of-the-art. |
URI: | http://hdl.handle.net/10045/96433 |
ISSN: | 0020-0255 (Print) | 1872-6291 (Online) |
DOI: | 10.1016/j.ins.2019.09.014 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 Elsevier Inc. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.ins.2019.09.014 |
Aparece en las colecciones: | INV - MVRLab - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2020_Curado_etal_InfSci_final.pdf | Versión final (acceso restringido) | 2,87 MB | Adobe PDF | Abrir Solicitar una copia |
2020_Curado_etal_InfSci_preprint.pdf | Preprint (acceso abierto) | 1,29 MB | Adobe PDF | Abrir Vista previa |
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