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
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
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
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
Repositori URV