Near-duplicate keyframe retrieval by nonrigid image matching
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Large margin training for hidden Markov models with partially observed states
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Learning structural SVMs with latent variables
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Equip tourists with knowledge mined from travelogues
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Multimodal location estimation
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MM '11 Proceedings of the 19th ACM international conference on Multimedia
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
What makes Paris look like Paris?
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Clues from the beaten path: Location estimation with bursty sequences of tourist photos
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SUN attribute database: Discovering, annotating, and recognizing scene attributes
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CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Discovering localized attributes for fine-grained recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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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.