Combining Weakly and Webly Supervised Learning for Classifying Food Images
This addresses the problem of scalable and cost-effective food classification for applications like dietary tracking, though it is incremental in combining existing techniques.
The paper tackled food image classification by combining a few curated samples with larger uncurated web datasets, improving top-1 accuracy from 50.3% to 72.8%, and further to 76.2% by adding weakly supervised learning to handle noisy labels.
Food classification from images is a fine-grained classification problem. Manual curation of food images is cost, time and scalability prohibitive. On the other hand, web data is available freely but contains noise. In this paper, we address the problem of classifying food images with minimal data curation. We also tackle a key problems with food images from the web where they often have multiple cooccuring food types but are weakly labeled with a single label. We first demonstrate that by sequentially adding a few manually curated samples to a larger uncurated dataset from two web sources, the top-1 classification accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we augment the deep model with Weakly Supervised learning (WSL) that results in an increase in performance to 76.2%. Finally, we show some qualitative results to provide insights into the performance improvements using the proposed ideas.