food-101

Mining Discriminative Components with Random Forests

A Matlab implementation of the Random Forest Discriminative Components algorithm described in [1] for automatically recognizing pictured dishes. The implementation achieves about a top-1 accuracy of 46% and a top-5 accuracy of 70% on the challenging food-101 dataset which contains 101000 images of 101 food categories. The dataset can be found here.

References:

• http://www.vision.ee.ethz.ch/datasets/food-101/static/bossard_eccv14_food-101.pdf