Abstract

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classifification. Among several sparsity models, treestructured sparsity provides a flflexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classifification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an effificient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for fifinding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classifification algorithms. To demonstrate their effificacy, the proposed methods are evaluated on three different applications – multiview face recognition, multimodal face recognition, and target classifification.