Semi-supervised Learning for Cross-Device Visual Location Recognition

The aim of this work is to localize a query mobile photograph by utilizing surveillance images, which naturally provide location information. We cast this cross-device visual localization problem as a classification task. By exploiting the surveillance network to collect reference images, the data acquisition process is significantly facilitated. However, the discrepancy between mobile images and surveillance images makes the training samples difficult to be used directly, and the scarcity of training samples caused by the immobility of surveillance cameras further degrades the performance. In contrast to most traditional domain adaptation problems and semi-supervised problems, the scarce labeled data and plentiful unlabeled data exist in different domains. Our location recognition method first exploits the unsupervised subspace alignment to weaken the discrepancy between the two domains, and then adopts the semi-supervised Laplacian SVM to reinforce the discriminant information utilizing the unlabeled mobile images. Experimental results show that our location recognition method significantly outperforms other related methods.