Geotagged image recognition by combining three different kinds of geolocation features

Abstract

Scenes and objects represented in photos have causal relationship to the places where they are taken. In this paper, we propose using geo-information such as aerial photos and location-related texts as features for geotagged image recognition and fusing them with Multiple Kernel Learning (MKL). By the experiments, we have verified the possibility for reflecting location contexts in image recognition by evaluating not only recognition rates, but feature fusion weights estimated by MKL. As a result, the mean average precision (MAP) for 28 categories increased up to 80.87% by the proposed method, compared with 77.71% by the baseline. Especially, for the categories related to location-dependent concepts, MAP was improved by 6.57 points. © 2011 Springer-Verlag Berlin Heidelberg.