Multimodal Dictionary Learning and Joint Sparse Representation for HEp-2 Cell Classification

Use of automatic classification for Indirect Immunofluorescence (IIF) images of HEp-2 cells is increasingly gaining interest in Antinuclear Autoantibodies (ANAs) detection. In order to improve the classification accuracy, we propose a multi-modal joint dictionary learning method, to obtain a discriminative and reconstructive dictionary while training a classifier simultaneously. Here, the term ‘multi-modal’ refers to features extracted using different algorithms from the same data set. To utilize information fusion between feature modalities the algorithm is designed so that sparse codes of all modalities of each sample share the same sparsity pattern. The contribution of this paper is two-fold. First, we propose a new framework for multi-modal fusion at the feature level. Second, we impose an additional constraint on consistency of sparse coefficients among different modalities of the same class. Extensive experiments are conducted on the ICPR2012 and ICIP2013 HEp-2 datasets. All results confirm the higher level of accuracy of the proposed method compared with state-of-the-art.