Geolocalized Modeling for Dish Recognition
|Name||Geolocalized Modeling for Dish Recognition|
Food recognition is still not accurate enough. Most works focus on exploiting only the visual content while ignoring the context. We propose a framework incorporating discriminative classification in geolocalized settings and introduce the concept of geolocalized models, which, in our scenario, are trained locally at each restaurant location. We collected a restaurant-oriented food dataset with food images, dish tags, and restaurant-level information, such as the menu and geolocation. Experiments on this dataset show that exploiting geolocation improves around 30% the recognition performance, and geolocalized models contribute with an additional 3-8% absolute gain, while they can be trained up to five times faster.
|ieee paper year||2016|