  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.
