A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Exploratory image databases: content-based retrieval
Exploratory image databases: content-based retrieval
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Diversifying the image retrieval results
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A translation model for sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Aspects of sentence retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Boosting image retrieval through aggregating search results based on visual annotations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Improving Folksonomies Using Formal Knowledge: A Case Study on Search
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Diversifying web search results
Proceedings of the 19th international conference on World wide web
Faceted exploration of image search results
Proceedings of the 19th international conference on World wide web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Topic based photo set retrieval using user annotated tags
Multimedia Tools and Applications
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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. The contribution of this paper is twofold. First, we present a retrieval model which provides diverse results as a property of the model itself, rather than in a post-retrieval step. Relevance models offer a unified framework to afford the greatest diversity without harming precision. Second, we show that it is possible to minimize the trade-offs between precision and diversity, and estimating the query model from the distribution of tags favors the dominant sense of a query. Relevance models operating only on tags offers the highest level of diversity with no significant decrease in precision.