For the CLEF 2006 Cross Language Image Retrieval (ImageCLEF) Photo Collection Standard Ad Hoc task, DCU performed monolingual and cross language retrieval using photo annotations with and without feedback, and also a combined visual and text retrieval approach. Topics are translated into English using the Babelfish online machine translation system. Text runs used the BM25 algorithm, while visual approach used simple low-level features with matching based on the Jeffrey Divergence measure. Our results consistently indicate that the fusion of text and visual features is best for this task, and that performing feedback for text consistently improves on the baseline non-feedback BM25 text runs for all language pairs.