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Food places classification in egocentric images using Siamese neural networks

  • Datos identificativos

    Identificador: imarina:6390078
    Autores:
    Kamal Sarker, Md MostafaFurruka Banu, SyedaRashwan, Hatem AAbdel-Nasser, MohamedKumar Singh, VivekChambon, SylvieRadeva, PetiaPuig, Domenec
    Resumen:
    © 2019 The authors and IOS Press. All rights reserved. Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with'MiniEgoFoodPlaces' with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
  • Otros:

    Autor según el artículo: Kamal Sarker, Md Mostafa; Furruka Banu, Syeda; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Chambon, Sylvie; Radeva, Petia; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Banu, Syeda Furruka / Puig Valls, Domènec Savi
    Palabras clave: Siamese neural networks Scene classification One-shot learning Food pattern classification Egocentric vision
    Resumen: © 2019 The authors and IOS Press. All rights reserved. Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with'MiniEgoFoodPlaces' with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
    Áreas temáticas: Medicina ii Interdisciplinar Información y documentación General o multidisciplinar Engenharias iv Engenharias iii Comunicació i informació Ciências agrárias i Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205
    Fecha de alta del registro: 2024-09-21
    Versión del articulo depositado: info:eu-repo/semantics/submittedVersion
    Enlace a la fuente original: https://ebooks.iospress.nl/publication/52830
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 319 145-151
    Referencia de l'ítem segons les normes APA: Kamal Sarker, Md Mostafa; Furruka Banu, Syeda; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Chambon, Sylvie; Radeva, Petia; Puig, Dome (2019). Food places classification in egocentric images using Siamese neural networks. Amsterdam: IOS Press
    DOI del artículo: 10.3233/FAIA190117
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    Artificial Intelligence
    Siamese neural networks
    Scene classification
    One-shot learning
    Food pattern classification
    Egocentric vision
    Medicina ii
    Interdisciplinar
    Información y documentación
    General o multidisciplinar
    Engenharias iv
    Engenharias iii
    Comunicació i informació
    Ciências agrárias i
    Artificial intelligence
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