Giant

This paper considers the problem of automatically discovering geo-informative attributes for location recognition and exploration. The attribute is expected to be both discriminative and representative, which corresponds to a distinctive visual pattern and associates with semantic interpretation. For solution, we analyze the attribute at region level. Each segmented region in the training set is assigned a binary latent variable indicating its discriminative capability. A latent learning framework is proposed for discriminative region detection and geo-informative attribute discovery. Moreover, we use user-generated content to obtain the semantic interpretation for the discovered visual attribute. The proposed approach are evaluated on one challenging dataset including GoogleStreetView and Flickr photos. Experimental results show that: (1) geo-informative attribute are discriminative and useful for location recognition; (2) the discovered semantic interpretation is meaningful and can be exploited for further explorations.