Most photo sharing sites give their users the opportunity to manually label images. The labels collected that way are usually very incomplete due to the size of the image collections: most images are not labeled according to all the categories they belong to, and, conversely, many class have relatively few representative examples. Automated image systems that can deal with small amounts of labeled examples and unbalanced classes are thus necessary to better organize and annotate images. In this work, we propose a multiview semi-supervised bipartite ranking model which allows to leverage the information contained in unlabeled sets of images in order to improve the prediction performance, using multiple descriptions, or views of images. For each topic class, our approach first learns as many view-specific rankers as available views using the labeled data only. These rankers are then improved iteratively by adding pseudo-labeled pairs of examples on which all view-specific rankers agree over the ranking of examples within these pairs. We report on experiments carried out on the NUS-WIDE dataset, which show that the multiview ranking process improves predictive performances when a small number of labeled examples is available specially for unbalanced classes. We show also that our approach achieves significant improvements over a state-of the art semi-supervised multiview classification model.