Relevance feedback for cross language image retrieval

In this paper we show how relevance feedback can be used to improve retrieval performance for a cross language image retrieval task through query expansion. This area of CLIR is different from existing problems, but has thus far received little attention from CLIR researchers. Using the ImageCLEF test collection, we simulate user interaction with a CL image retrieval system, and in particular the situation in which a user selects one or more relevant images from the top n. Using textual captions associated with the images, relevant images are used to create a feedback model in the Lemur language model for information retrieval, and our results show that feedback is beneficial, even when only one relevant document is selected. This is particularly useful for cross language retrieval where problems during translation can result in a poor initial ranked list with few relevant in the top n. We find that the number of feedback documents and the influence of the initial query on the feedback model most affect retrieval performance.