TY  - JOUR
AU  - Eduardo Aguilar
AU  - Petia Radeva
PY  - 2020//
TI  - Uncertainty-aware integration of local and flat classifiers for food recognition
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 237
EP  - 243
VL  - 136
N2  - Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
UR  - https://doi.org/10.1016/j.patrec.2020.06.013
N1  - MILAB; no proj
ID  - Eduardo Aguilar2020
ER  -