| KEYWORD |
Automatic classification of images of food
keywords DEEP NEURAL NETWORKS, IMAGE PROCESSING, MACHINE LEARNING
Reference persons ANDREA BOTTINO, MAURIZIO MORISIO
Research Groups GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG
Thesis type EXPERIMENTAL / DEVELOPMENT
Description In many contexts it is important to track what a person eats (illnesses, fitness, allergies etc). A practical way to do this is to take a picture of the food eaten using a smartphone and having an automatic classifier capable of recognizing the food with high accuracy. Having recognized the food it is then possible to know the basic ingredients eaten (proteins, fats etc). Beyond food classification another open problem is characterizing the quantity eaten.
This thesis consists in selecting a suitable open data set of tagged food pictures (ex Recipe1M+), trying various machine learning / deep learning techniques to build a classifier, using image processing techniques to infer the quantity of food eaten, with the goal of achieving the highest accuracy both in food classification and quantity computation.
Required skills Python, Java, machine learning approaches and libraries, focusing on image processing, image classification
Deadline 07/11/2024
PROPONI LA TUA CANDIDATURA