Articles producció científica> Enginyeria Informàtica i Matemàtiques

Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism

  • Datos identificativos

    Identificador: imarina:5643786
    Autores:
    Sarker, Md Mostafa KamalRashwan, Hatem AAkram, FarhanTalavera, EstefaniaBanu, Syeda FurrukaRadeva, PetiaPuig, Domenec
    Resumen:
    © 2013 IEEE. Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called 'EgoFoodPlaces' that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the 'EgoFoodPlaces' dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3.
  • Otros:

    Autor según el artículo: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Talavera, Estefania; Banu, Syeda Furruka; Radeva, Petia; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / AKRAM, FARHAN / Banu, Syeda Furruka / Puig Valls, Domènec Savi
    Palabras clave: Visual lifelogging Self-attention model Scene classification Scene Obesity Food places recognition Egocentric photo-streams Classification Atrous convolutional networks
    Resumen: © 2013 IEEE. Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called 'EgoFoodPlaces' that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the 'EgoFoodPlaces' dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3.
    Áreas temáticas: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 21693536
    Direcció de correo del autor: hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
    Identificador del autor: 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/publishedVersion
    Enlace a la fuente original: https://ieeexplore.ieee.org/document/8671710
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Ieee Access. 7 39069-39082
    Referencia de l'ítem segons les normes APA: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Talavera, Estefania; Banu, Syeda Furruka; Radeva, Petia; Puig, Domenec (2019). Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism. Ieee Access, 7(), 39069-39082. DOI: 10.1109/ACCESS.2019.2902225
    DOI del artículo: 10.1109/ACCESS.2019.2902225
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science (Miscellaneous),Computer Science, Information Systems,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Materials Science (Miscellaneous),Telecommunications
    Visual lifelogging
    Self-attention model
    Scene classification
    Scene
    Obesity
    Food places recognition
    Egocentric photo-streams
    Classification
    Atrous convolutional networks
    Telecommunications
    Materials science (miscellaneous)
    Materials science (all)
    General materials science
    General engineering
    General computer science
    Engineering, electrical & electronic
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Computer science, information systems
    Computer science (miscellaneous)
    Computer science (all)
    Ciência da computação
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