Indirect immunofluorescence (IIF) imaging is a method used for detection of antinuclear auto-antibodies (ANA) for the diagnosis of autoimmune diseases. We present a feature extraction and classification scheme to classify the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary features that are sensitive to staining pattern variations among classes. Our feature set utilizes local shape measures via Hessian matrix, gradient features using our adaptive robust structure tensors and texture features. We apply our multi-view ShareBoost algorithm to this set using each feature descriptor as a separate view. ShareBoost utilizes a single re-sampling distribution for all views that helps the classifier to exploit the interplay between subspaces and is robust to noisy labels. Our experimental results show an average of over 90 percent accuracy in classification of six HEp-2 cell types.