Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
A study of learning a merge model for multilingual information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task
Advances in Multilingual and Multimodal Information Retrieval
Overview of the ImageCLEFphoto 2008 photographic retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
The CLEF 2005 cross–language image retrieval track
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
The CLEF 2004 cross-language image retrieval track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Language translation and media transformation in cross-language image retrieval
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
Overview of the ImageCLEF 2006 photographic retrieval and object annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
What Else Is There? Search Diversity Examined
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
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This paper considers the strategies of query expansion, relevance feedback and result fusion to increase both precision and diversity in photo retrieval. In the text-based retrieval only experiments, the run with query expansion has better MAP and P20 than that without query expansion, and only has 0.85% decrease in CR20. Although relevance feedback run increases both MAP and P20, its CR20 decreases 10.18% compared with the non-feedback run. It shows that relevance feedback brings in relevant but similar images, thus diversity may be decreased. The run with both query expansion and relevance feedback is the best in the four text-based runs. Its F1-measure is 0.2791, which has 20.8% increase to the baseline model. In the content-based retrieval only experiments, the run without feedback outperforms the run with feedback. The latter has 10.84%, 9.13%, 20.46%, and 16.7% performance decrease in MAP, P20, CR20, and F1-measure. In the fusion experiment, integrating text-based and content-based retrieval not only reports more relevant images, but also more diverse ones. Its F1-measure is 0.3189.