Users commonly rely just on scarce textual annotation when their searches for images are semantic or conceptual based. Rich visual information is often thrown away in basic annotation-based image retrieval because its relationship to the semantic content is not always clear. To ensure that appropriate visual information is included, we propose using visual clustering within pre-processing and post-processing steps of text-based retrieval. A clustering algorithm finds pairs of images that are nearly identical and are, therefore, presumed semantically similar. The output from basic retrieval systems is a ranked list of images based only on lexical term matching. The obtained cluster knowledge is then used to modify the ranking result during the post-processing step. Low ranked images considered nearly identical to more highly ranked images are then pulled up. The modularity of this architecture allows us to integrate a data mining process without having to change core information retrieval systems. Evaluation on a cross-language image retrieval test collection showed that this method improved retrieval performance for certain queries in multilingual settings.