Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context data in geo-referenced digital photo collections
Proceedings of the 12th annual ACM international conference on Multimedia
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Spirittagger: a geo-aware tag suggestion tool mined from flickr
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Automatic tag expansion using visual similarity for photo sharing websites
Multimedia Tools and Applications
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Tag recommendation for georeferenced photos
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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In this paper, we address the problem of landmark image annotation, defined as the task of automatically annotating a landmark query image with relevant descriptors (keywords or tags). Given a new query image along with its geolocation metadata (latitude and longitude), we retrieve several other images already available in a community image database (e.g., flickr.com, panoramio.com), found within a fixed radius of the location of the query image. We then formulate the automatic landmark image annotation problem as a tag ranking problem over all the tags obtained from these pre-existing neighboring images. We propose several tag ranking factors, and by evaluating them against a gold standard constructed using the geolocation-oriented photo sharing platform panoramio.com, we show that an aggregated measure that combines both distance and frequency factors leads to results significantly better than any of the individual factors.