FoodNet: Recognizing Foods Using Ensemble of Deep Networks
This work addresses food recognition for applications like dietary monitoring, but it is incremental as it builds on existing CNN methods with feature fusion.
The authors tackled the problem of automatic food classification from images by developing a multi-layered deep convolutional neural network (CNN) architecture that fuses features from other deep networks to improve efficiency, achieving higher accuracy on the ETH Food-101 and a new Indian food image database compared to benchmark CNN frameworks.
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.