Geotagged iage rcognition by cmbining tree dfferent knds of golocation fatures

Authors:
Keita Yaegashi;Keiji Yanai
Affiliations:
Department of Computer Science, The University of Electro-Communications, Chofu-hi, Tokyo, Japan;Department of Computer Science, The University of Electro-Communications, Chofu-hi, Tokyo, Japan
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Year:
2010

Citing 8
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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.