Shape-Preserving Generation of Food Images for Automatic Dietary Assessment
This work addresses the problem of data scarcity for training AI models in dietary assessment, which is incremental as it builds on existing GAN techniques for image generation.
The paper tackles the challenge of generating large amounts of realistic food images with known volumes for automatic dietary assessment by proposing a simple GAN-based neural network architecture that preserves shapes from reference images, demonstrating realism and shape-preserving capabilities in experiments.
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.