%0 Conference Proceedings
%T Food Places Classification in Egocentric Images Using Siamese Neural Networks
%A Md. Mostafa Kamal Sarker
%A Syeda Furruka Banu
%A Hatem A. Rashwan
%A Mohamed Abdel-Nasser
%A Vivek Kumar Singh
%A Sylvie Chambon
%A Petia Radeva
%A Domenec Puig
%B 22nd International Conference of the Catalan Association of Artificial Intelligence
%D 2019
%F Md. Mostafa Kamal Sarker2019
%O MILAB; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3368), last updated on Fri, 05 Jul 2024 12:35:11 +0200
%X 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.
%U http://dx.doi.org/10.3233/FAIA190117
%P 145-151