Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Image retrieval on large-scale image databases
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Gazetiki: automatic creation of a geographical gazetteer
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Object identification and retrieval from efficient image matching: Snap2Tell with the STOIC dataset
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
The InfoAlbum image centric information collection
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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Uploading tourist photographs is a popular activity on photo sharing platforms. The manual annotation of these images is a tedious process and the users often upload their images with no associated textual information. Automating the annotation process has received a lot of attention but the problem remains a hard one, especially when dealing with large and heterogeneous databases. Here we focus on landmarks images, very frequent among tourism pictures, and propose a new automatic technique for annotating this type of pictures. Our system, called MonuAnno, relies on the joint exploitation of localization information and of image content analysis in an efficient and scalable framework. The annotation is performed using a two steps k Nearest Neighbors (k-NN). First, only neighboring landmarks of a new unlabeled georeferenced image will be considered as potential annotations and the image will be attributed to the landmark that is visually closest. Then, we introduce a verification step that eliminates false positives (images taken near a landmark that represent something else). The technique was tested on Web images and the results show that the precision of the labeling process in MonuAnno exceeds 80%, when annotating around 50% of the images in the test set.