Learning Geographical Hierarchy Features for Social Image Location Prediction / 2401
Xiaoming Zhang, Xia Hu, Zhoujun Li
Image location prediction is to estimate the geolocation where an image is taken. Social image contains heterogeneous contents, which makes image location prediction nontrivial. Moreover, it is observed that image content patterns and location preferences correlate hierarchically. Traditional image location prediction methods mainly adopt a single-level architecture, which is not directly adaptable to the hierarchical correlation. In this paper, we propose a geographically hierarchical bi-modal deep belief network model (GH-BDBN), which is a compositional learning architecture that integrates multi-modal deep learning model with non-parametric hierarchical prior model. GH-BDBN learns a joint representation capturing the correlations among different types of image content using a bi-modal DBN, with a geographically hierarchical prior over the joint representation to model the hierarchical correlation between image content and location. Experimental results demonstrate the superiority of our model for image location prediction.