Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Retrieving lightly annotated images using image similarities
Proceedings of the 2005 ACM symposium on Applied computing
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
Caption and query translation for cross-language image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
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
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When short queries and short image annotations are used in text-based cross-language image retrieval, small changes in word usage due to translation errors may decrease the retrieval performance because of an increase in lexical mismatches. In the ImageCLEF2005 ad-hoc task, we investigated the use of learned word association models that represent how pairs of words are related to absorb such mismatches. We compared a precision-oriented simple word-matching retrieval model and a recall-oriented word association retrieval model. We also investigated combinations of these by introducing a new ranking function that generated comparable output values from both models. Experimental results on English and German topics were discouraging, as the use of word association models degraded the performance. On the other hand, word association models helped retrieval for Japanese topics whose translation quality was low.