Food places classification in egocentric images using Siamese neural networks.

Wearable cameras have become more popular in recent years for capturing unscripted moments in the first-person, which help in analysis of the user's lifestyle. In this work, we aim to identify the daily food patterns of a person through recognition of places relating to food in person-focused images ("selfies"). This has the potential for a system that can assist with improvements to eating habits and prevention of diet-related conditions. In this paper, we use Siamese Neural Networks (SNN) to learn similarities between images with one-shot "food places" classification. We tested our proposed method with "MiniEgoFoodPlaces", using 15 food-related locations. The proposed SNN model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the "MiniEgoFoodPlaces" dataset, outperforming the base models such as ResNet50, InceptionV3 and InceptionResNetV2