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Obtaining the consensus of multiple correspondences between graphs through online learning

  • Datos identificativos

    Identificador: imarina:9282636
    Autores:
    Francisco Moreno-Garcia, CarlosSerratosa, Francesc
    Resumen:
    In structural pattern recognition, it is usual to compare a pair of objects through the generation of a correspondence between the elements of each of their local parts. To do so, one of the most natural ways to represent these objects is through attributed graphs. Several existing graph extraction methods could be implemented and thus, numerous graphs, which may not only differ in their nodes and edge structure but also in their attribute domains, could be created from the same object. Afterwards, a matching process is implemented to generate the correspondence between two attributed graphs, and depending on the selected graph matching method, a unique correspondence is generated from a given pair of attributed graphs. The combination of these factors leads to the possibility of a large quantity of correspondences between the two original objects. This paper presents a method that tackles this problem by considering multiple correspondences to conform a single one called a consensus correspondence, eliminating both the incongruences introduced by the graph extraction and the graph matching processes. Additionally, through the application of an online learning algorithm, it is possible to deduce some weights that influence on the generation of the consensus correspondence. This means that the algorithm automatically learns the quality of both the attribute domain and the correspondence for every initial correspondence proposal to be considered in the consensus, and defines a set of weights based on this quality. It is shown that the method automatically tends to assign larger values to high quality initial proposals, and therefore is capable to deduce better consensus correspondences. © 2016 Elsevier B.V.
  • Otros:

    Autor según el artículo: Francisco Moreno-Garcia, Carlos; Serratosa, Francesc
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: MORENO GARCIA, CARLOS FRANCISCO / Serratosa Casanelles, Francesc d'Assís
    Palabras clave: Structural pattern recognition Sets Pattern matching Online learning algorithms Online learning Learning algorithms Graphic methods Graph-matching methods Graph matchings Graph matching Graph extractions Features Extraction E-learning Database Consensus correspondence Computation Attributed graphs
    Resumen: In structural pattern recognition, it is usual to compare a pair of objects through the generation of a correspondence between the elements of each of their local parts. To do so, one of the most natural ways to represent these objects is through attributed graphs. Several existing graph extraction methods could be implemented and thus, numerous graphs, which may not only differ in their nodes and edge structure but also in their attribute domains, could be created from the same object. Afterwards, a matching process is implemented to generate the correspondence between two attributed graphs, and depending on the selected graph matching method, a unique correspondence is generated from a given pair of attributed graphs. The combination of these factors leads to the possibility of a large quantity of correspondences between the two original objects. This paper presents a method that tackles this problem by considering multiple correspondences to conform a single one called a consensus correspondence, eliminating both the incongruences introduced by the graph extraction and the graph matching processes. Additionally, through the application of an online learning algorithm, it is possible to deduce some weights that influence on the generation of the consensus correspondence. This means that the algorithm automatically learns the quality of both the attribute domain and the correspondence for every initial correspondence proposal to be considered in the consensus, and defines a set of weights based on this quality. It is shown that the method automatically tends to assign larger values to high quality initial proposals, and therefore is capable to deduce better consensus correspondences. © 2016 Elsevier B.V.
    Áreas temáticas: Software Signal processing Medicina iii Medicina ii Matemática / probabilidade e estatística Interdisciplinar Geociências Engenharias iv Engenharias iii Educação física Educação Computer vision and pattern recognition Computer science, artificial intelligence Ciências biológicas i Ciências ambientais Ciência da computação Biotecnología Biodiversidade Astronomia / física Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: francesc.serratosa@urv.cat
    Identificador del autor: 0000-0001-6112-5913
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0167865516302367
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Pattern Recognition Letters. 87 79-86
    Referencia de l'ítem segons les normes APA: Francisco Moreno-Garcia, Carlos; Serratosa, Francesc (2017). Obtaining the consensus of multiple correspondences between graphs through online learning. Pattern Recognition Letters, 87(), 79-86. DOI: 10.1016/j.patrec.2016.09.003
    DOI del artículo: 10.1016/j.patrec.2016.09.003
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2017
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
    Structural pattern recognition
    Sets
    Pattern matching
    Online learning algorithms
    Online learning
    Learning algorithms
    Graphic methods
    Graph-matching methods
    Graph matchings
    Graph matching
    Graph extractions
    Features
    Extraction
    E-learning
    Database
    Consensus correspondence
    Computation
    Attributed graphs
    Software
    Signal processing
    Medicina iii
    Medicina ii
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    Engenharias iv
    Engenharias iii
    Educação física
    Educação
    Computer vision and pattern recognition
    Computer science, artificial intelligence
    Ciências biológicas i
    Ciências ambientais
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
    Artificial intelligence
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