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Título
One Shot Learning with class partitioning and cross validation voting (CP-CVV)
Año del Documento
2023
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
Pattern Recognition, 2023, vol. 143, 109797
Resumo
One 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.
Materias (normalizadas)
Artificial intelligence
Image Processing and Computer Vision
Materias Unesco
33 Ciencias Tecnológicas
Palabras Clave
One shot learning
Grocery Product Classification
Wide ResNet
Clasificación de productos de alimentación
ResNet amplia
ISSN
0031-3203
Revisión por pares
SI
Patrocinador
The 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)
Propietario de los Derechos
© 2023 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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