Diversifying image search with user generated content Export

@inproceedings{zwol_2008_diversifying, abstract = {Large-scale image retrieval on the Web relies on the availability of short snippets of text associated with the image. This user-generated content is a primary source of information about the content and context of an image. While traditional information retrieval models focus on finding the most relevant document without consideration for diversity, image search requires results that are both diverse and relevant. This is problematic for images because they are represented very sparsely by text, and as with all user-generated content the text for a given image can be extremely noisy.}, address = {New York, NY, USA}, author = {van Zwol, Roelof and Murdock, Vanessa and Pueyo, Lluis G. and Ramirez, Georgina}, booktitle = {MIR '08: Proceeding of the 1st ACM international conference on Multimedia information retrieval}, citeulike-article-id = {3920677}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1460096.1460109}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1460096.1460109}, comment = {avg 32 terms per photo including title, description and tags avg 10 tags per photo avg title of 3 terms [my summary] Van Zwol et al.\~{}\cite{vanzwol08diversifying} study diversity in the context of image search in a social media site\footnote{http://flickr.com/}. A set of 25 ambiguous queries (including apple, jaguar, etc.) are issued to multiple retrieval systems and the top results annotated by human assessors, both in terms of precision but also grouping the results into senses. Next, methods are compared both in terms of precision and in terms of their power to bring to the top of the results lists different senses for a query. Retrieval systems built only on the tags image receive, generate a more diverse set of results than systems based on the other information available (title and description). }, doi = {10.1145/1460096.1460109}, isbn = {978-1-60558-312-9}, keywords = {image, ranking, search, similarity, tagging}, location = {Vancouver, British Columbia, Canada}, pages = {67--74}, posted-at = {2009-01-21 18:08:35}, priority = {2}, publisher = {ACM}, title = {Diversifying image search with user generated content}, url = {http://dx.doi.org/10.1145/1460096.1460109}, year = {2008} }

TY - CONF ID - zwol_2008_diversifying L3 - citeulike-article-id:3920677 N2 - Large-scale image retrieval on the Web relies on the availability of short snippets of text associated with the image. This user-generated content is a primary source of information about the content and context of an image. While traditional information retrieval models focus on finding the most relevant document without consideration for diversity, image search requires results that are both diverse and relevant. This is problematic for images because they are represented very sparsely by text, and as with all user-generated content the text for a given image can be extremely noisy. CY - Vancouver, British Columbia, Canada SN - 978-1-60558-312-9 AD - New York, NY, USA EP - 74 TI - Diversifying image search with user generated content PB - ACM T2 - MIR '08: Proceeding of the 1st ACM international conference on Multimedia information retrieval SP - 67 KW - image KW - ranking KW - search KW - similarity KW - tagging N1 - avg 32 terms per photo including title, description and tags avg 10 tags per photo avg title of 3 terms [my summary] Van Zwol et al.~\cite{vanzwol08diversifying} study diversity in the context of image search in a social media site\footnote{http://flickr.com/}. A set of 25 ambiguous queries (including apple, jaguar, etc.) are issued to multiple retrieval systems and the top results annotated by human assessors, both in terms of precision but also grouping the results into senses. Next, methods are compared both in terms of precision and in terms of their power to bring to the top of the results lists different senses for a query. Retrieval systems built only on the tags image receive, generate a more diverse set of results than systems based on the other information available (title and description). AU - van Zwol, Roelof AU - Murdock, Vanessa AU - Pueyo, Lluis AU - Ramirez, Georgina PY - 2008/// UR - http://dx.doi.org/10.1145/1460096.1460109 ER -