Shape-Preserving Generation of Food Images for Automatic Dietary Assessment

TL;DR

This paper introduces a GAN-based method for generating realistic, shape-preserving food images to aid automatic dietary assessment, addressing data scarcity issues in training models for food recognition and volume estimation.

Contribution

The work proposes a simple conditional GAN architecture that generates realistic food images with preserved shapes, facilitating data augmentation for dietary assessment tasks.

Findings

Generated images are realistic and shape-preserving.

The framework effectively mimics reference food shapes.

The method demonstrates potential for improving dietary assessment models.

Abstract

Traditional dietary assessment methods heavily rely on self-reporting, which is time-consuming and prone to bias. Recent advancements in Artificial Intelligence (AI) have revealed new possibilities for dietary assessment, particularly through analysis of food images. Recognizing foods and estimating food volumes from images are known as the key procedures for automatic dietary assessment. However, both procedures required large amounts of training images labeled with food names and volumes, which are currently unavailable. Alternatively, recent studies have indicated that training images can be artificially generated using Generative Adversarial Networks (GANs). Nonetheless, convenient generation of large amounts of food images with known volumes remain a challenge with the existing techniques. In this work, we present a simple GAN-based neural network architecture for conditional food image generation. The shapes of the food and container in the generated images closely resemble those in the reference input image. Our experiments demonstrate the realism of the generated images and shape-preserving capabilities of the proposed framework.