Articles producció científica> Enginyeria Informàtica i Matemàtiques

Interactive online learning for graph matching using active strategies

  • Dades identificatives

    Identificador: imarina:6961617
  • Autors:

    Conte D
    Serratosa F
  • Altres:

    Autor segons l'article: Conte D; Serratosa F
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Serratosa Casanelles, Francesc d'Assís
    Paraules clau: Optimality Online learning Models Human interaction Graph matching Edit distance Costs functions Costs Cooperative pose estimation Computation Assignment Algorithms Active learning
    Resum: © 2020 Elsevier B.V. In some pattern recognition applications, objects are represented by attributed graphs, in which nodes represent local parts of the objects and edges represent relationships between these local parts. In this framework, the comparison between objects is performed through the distance between attributed graphs. Usually, this distance is a linear equation defined by some cost functions on the nodes and on the edges of both attributed graphs. In this paper, we present an online, active and interactive method for learning these cost functions, which works as follows. Graphs are provided to the learning algorithm by pairs in a sequential order (online). Then, a correspondence between them is computed, and there is a strategy that, given the current pair of graphs and the computed correspondence, proposes which node-to-node mapping would most contribute to the learning process (active). Finally, the human can correct some node-to-node mappings if the human thinks they are wrong (interactive). This is the first learning method applied to graph matching that has the following two features: Being an online method and being active and interactive. These properties make our method useful in the cases that data does not arrive at once and when the human can interact on the system. Thus, given some human interactions the method would have to tend to gradually increase its accuracy. The results show that with few interactions, we achieve better results than the offline learning state of the art methods that are currently available.
    Àrees temàtiques: Software Matemática / probabilidade e estatística Management information systems Interdisciplinar Information systems and management Información y documentación Engenharias iv Engenharias iii Economia Computer science, artificial intelligence Ciencias sociales Ciências biológicas i Ciência da computação Astronomia / física Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0950-7051
    Adreça de correu electrònic de l'autor: francesc.serratosa@urv.cat
    Identificador de l'autor: 0000-0001-6112-5913
    Data d'alta del registre: 2023-02-19
    Volum de revista: 205
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0950705120304585?via%3Dihub
    Referència a l'article segons font original: Knowledge-Based Systems. 205 (106275):
    Referència de l'ítem segons les normes APA: Conte D; Serratosa F (2020). Interactive online learning for graph matching using active strategies. Knowledge-Based Systems, 205(106275), -. DOI: 10.1016/j.knosys.2020.106275
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.knosys.2020.106275
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Information Systems and Management,Management Information Systems,Software
    Optimality
    Online learning
    Models
    Human interaction
    Graph matching
    Edit distance
    Costs functions
    Costs
    Cooperative pose estimation
    Computation
    Assignment
    Algorithms
    Active learning
    Software
    Matemática / probabilidade e estatística
    Management information systems
    Interdisciplinar
    Information systems and management
    Información y documentación
    Engenharias iv
    Engenharias iii
    Economia
    Computer science, artificial intelligence
    Ciencias sociales
    Ciências biológicas i
    Ciência da computação
    Astronomia / física
    Artificial intelligence
    Administração pública e de empresas, ciências contábeis e turismo
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