Food recognition a new dataset, experiments and results.(May 2017)

Aim:

The main stay of this project is to develop a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications.

Proposed system:

We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1,027 canteen trays for a total of 3,616 food instances belonging to 73 food classes. The food on the tray images have been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented three different classification strategies using also several visual descriptors.