Resolving translation ambiguity and target polysemy in cross-language information retrieval
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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
Integrating textual and visual information for cross-language image retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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
Dublin city university at CLEF 2004: experiments with the ImageCLEF st. andrew's collection
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Cross-media feedback strategies: merging text and image information to improve image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Increasing precision and diversity in photo retrieval by result fusion
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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Cross-language image retrieval facilitates the use of text query in one language and image query in one medium to access image collection with text description in another language/medium. The images with annotations are considered as a trans-media parallel corpus. In a media-mapping approach, we transform a query in one medium into a query in another medium by referencing to the aligned trans-media corpus. From the counterpart of results of an initial retrieval, we generate a new query in different medium. In the experiments, we adopted St. Andrews University Library's photographic collection used in ImageCLEF, and explored different models of language translation and media transformation. When both text query and image query are given together, the best MAP of a cross-lingual cross-media model 1L2M (one language translation plus two media transformations) achieve 87.15% and 72.39% of those of mono-lingual image retrieval in the 2004 and the 2005 test sets, respectively. That demonstrates our media transformation is quite useful, and it can compensate for the errors introduced in language translation.