FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Abstract

In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.

5 pages, 3 figures, 3 tables, IEEE Signal Processing Letters

References in corpus (1)

• Look Wider to Match Image Patches with Convolutional Neural Networks

Cited by in corpus (5)

• ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network
• Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation
• Ensemble Feature for Person Re-Identification
• An End-to-End Food Image Analysis System
• MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring