Easing Erroneous Translations in Cross-Language Image Retrieval Using Word Associations

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 adhoc 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.