ECCV 2014 - LNCS 8689-8695

Food-101 – Mining Discriminative Components with Random Forests

Lukas Bossard¹, Matthieu Guillaumin¹, and Luc Van Gool^{1, 2}

¹Computer Vision Lab, ETH Zürich, Switzerland
Bossard@vision.ee.ethz.ch
Guillaumin@vision.ee.ethz.ch

²ESAT, PSI-VISICS, K.U. Leuven, Belgium
vangool@esat.kuleuven.be

Abstract. In this paper we address the problem of automatically recognizing pictured dishes. To this end, we introduce a novel method to mine discriminative parts using Random Forests (rf), which allows us to mine for parts simultaneously for all classes and to share knowledge among them. To improve efficiency of mining and classification, we only consider patches that are aligned with image superpixels, which we call components. To measure the performance of our rf component mining for food recognition, we introduce a novel and challenging dataset of 101 food categories, with 101’000 images. With an average accuracy of 50.76%, our model outperforms alternative classification methods except for cnn, including svm classification on Improved Fisher Vectors and existing discriminative part-mining algorithms by 11.88% and 8.13%, respectively. On the challenging mit-Indoor dataset, our method compares nicely to other s-o-a component-based classification methods.

Keywords: Image classification, Discriminative part mining, Random Forest, Food recognition

LNCS 8694, p. 446 ff.

Full article in PDF | BibTeX