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dc.contributor.authorDuque Domingo, Jaime
dc.contributor.authorMedina Aparicio, Roberto
dc.contributor.authorGonzález Rodrigo, Luis Miguel
dc.date.accessioned2023-07-04T11:50:44Z
dc.date.available2023-07-04T11:50:44Z
dc.date.issued2023
dc.identifier.citationPattern Recognition, 2023, vol. 143, 109797es
dc.identifier.issn0031-3203es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/60065
dc.descriptionProducción Científicaes
dc.description.abstractOne Shot Learning includes all those techniques that make it possible to classify images using a single image per category. One of its possible applications is the identification of food products. For a grocery store, it is interesting to record a single image of each product and be able to recognise it again from other images, such as photos taken by customers. Within deep learning, Siamese neural networks are able to verify whether two images belong to the same category or not. In this paper, a new Siamese network training technique, called CP-CVV, is presented. It uses the combination of different models trained with different classes. The separation of validation classes has been done in such a way that each of the combined models is different in order to avoid overfitting with respect to the validation. Unlike normal training, the test images belong to classes that have not previously been used in training, allowing the model to work on new categories, of which only one image exists. Different backbones have been evaluated in the Siamese composition, but also the integration of multiple models with different backbones. The results show that the model improves on previous works and allows the classification problem to be solved, an additional step towards the use of Siamese networks. To the best of our knowledge, there is no existing work that has proposed integrating Siamese neural networks using a class-based validation set separation technique so as to be better at generalising for unknown classes. Additionally, we have applied Cross-Validation-Voting with ConvNeXt to improve the existing classification results of a well-known Grocery Store Dataset.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial intelligencees
dc.subjectImage Processing and Computer Visiones
dc.subject.classificationOne shot learninges
dc.subject.classificationGrocery Product Classificationes
dc.subject.classificationWide ResNetes
dc.subject.classificationClasificación de productos de alimentaciónes
dc.subject.classificationResNet ampliaes
dc.titleOne Shot Learning with class partitioning and cross validation voting (CP-CVV)es
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The Authorses
dc.identifier.doi10.1016/j.patcog.2023.109797es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0031320323004958?via%3Dihubes
dc.identifier.publicationfirstpage109797es
dc.identifier.publicationtitlePattern Recognitiones
dc.peerreviewedSIes
dc.description.projectThe Centre for the Development of Industrial Technology (CDTI) and by the Instituto para la Competitividad Empresarial de Castilla y León - FEDER (Project CCTT3/20/VA/0003)es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco33 Ciencias Tecnológicases


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