Articles producció científica> Enginyeria Informàtica i Matemàtiques

Food places classification in egocentric images using Siamese neural networks

  • Dades identificatives

    Identificador: imarina:6390078
    Handle: https://hdl.handle.net/20.500.11797/imarina6390078
    Autors:
    Kamal Sarker, Md MostafaFurruka Banu, SyedaRashwan, Hatem AAbdel-Nasser, MohamedKumar Singh, VivekChambon, SylvieRadeva, PetiaPuig, Domenec
    Resum:
    © 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.

  • Altres:

    Autor segons l'article: Kamal Sarker, Md Mostafa; Furruka Banu, Syeda; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Chambon, Sylvie; Radeva, Petia; Puig, Domenec
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Banu, Syeda Furruka / Puig Valls, Domènec Savi
    Paraules clau: Siamese neural networks Scene classification One-shot learning Food pattern classification Egocentric vision
    Resum: © 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.
    Àrees temàtiques: Medicina ii Interdisciplinar Información y documentación General o multidisciplinar Engenharias iv Engenharias iii Comunicació i informació Ciências agrárias i Artificial intelligence
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205
    Data d'alta del registre: 2024-09-21
    Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
    Enllaç font original: https://ebooks.iospress.nl/publication/52830
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Frontiers In Artificial Intelligence And Applications. 319 145-151
    Referència 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 de l'article: 10.3233/FAIA190117
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Proceedings Paper

  • Paraules clau:

    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

  • Documents:

    DocumentPrincipal

  • Cerca a google

    Search to google scholar