Autor segons l'article: Francisco Moreno-Garcia, Carlos; Serratosa, Francesc
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: MORENO GARCIA, CARLOS FRANCISCO / Serratosa Casanelles, Francesc d'Assís
Paraules clau: 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
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: francesc.serratosa@urv.cat
Identificador de l'autor: 0000-0001-6112-5913
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0167865516302367
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Pattern Recognition Letters. 87 79-86
Referència 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 de l'article: 10.1016/j.patrec.2016.09.003
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2017
Tipus de publicació: Journal Publications