TY  - JOUR
AU  - Luis Herranz
AU  - Shuqiang Jiang
AU  - Ruihan Xu
PY  - 2017//
TI  - Modeling Restaurant Context for Food Recognition
T2  - TMM
JO  - IEEE Transactions on Multimedia
SP  - 430
EP  - 440
VL  - 19
IS  - 2
N2  - Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks.
L1  - http://refbase.cvc.uab.es/files/HJX2017.pdf
UR  - http://dx.doi.org/10.1109/TMM.2016.2614861
N1  - LAMP; 600.120
ID  - Luis Herranz2017
ER  -