Repositori institucional URV
| URV's Author/s: | Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Banu, Syeda Furruka / Puig Valls, Domènec Savi |
| Author, as appears in the article.: | Kamal Sarker, Md Mostafa; Furruka Banu, Syeda; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Chambon, Sylvie; Radeva, Petia; Puig, Domenec |
| Author's mail: | mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat |
| Author identifier: | 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205 |
| Journal publication year: | 2019 |
| Publication Type: | Proceedings Paper |
| 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 |
| Papper original source: | Frontiers In Artificial Intelligence And Applications. 319 145-151 |
| Abstract: | © 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. |
| Article's DOI: | 10.3233/FAIA190117 |
| Link to the original source: | https://ebooks.iospress.nl/publication/52830 |
| Papper version: | info:eu-repo/semantics/submittedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Medicina ii Interdisciplinar Información y documentación General o multidisciplinar Engenharias iv Engenharias iii Comunicació i informació Ciências agrárias i Artificial intelligence |
| Keywords: | Siamese neural networks Scene classification One-shot learning Food pattern classification Egocentric vision |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-21 |
| Search your record at: |   |
| File | Description | Format | |
|---|---|---|---|
| DocumentPrincipal | DocumentPrincipal | application/pdf |
© 2011 Universitat Rovira i Virgili