LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Associating Faces and Names in Japanese Photo News Articles on the Web
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
Unsupervised Face Annotation by Mining the Web
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Interesting faces: A graph-based approach for finding people in news
Pattern Recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We consider the automatic annotation of faces of people mentioned in news. News stories provide a constant flow of potentially useful image indexing information, due to their huge diffusion on the web and to the involvement of human operators in selecting relevant images for the stories. In this work we investigate the possibility of actually exploiting this wealth of information. We propose and evaluate a system for automatic face annotation of image news that is fully unsupervised and does not require any prior knowledge about topic or people involved. Key feature of our proposal is that it attempts to identify the essential piece of information -- how a person with a given name looks like -- by querying popular image search engines. Mining the web allows overcoming intrinsic limitations of approaches built above a predefined collection of stories: our system can potentially annotate people never handled before since its knowledge base is constantly expanded, as long as search engines keep on indexing the web. On the other hand, leveraging on image search engines forces to cope with the substantial amount of noise in search engine results. Our contribution shows experimentally that automatic face annotation may indeed be achieved based entirely on knowledge that lives in the web.