A Multiview Joint Sparse Representation with Discriminative Dictionary for Melanoma Detection
Great efforts have been made over the last few decades to promote the development of computer aided diagnosis systems for melanoma detection. However, most of these systems employ multiple descriptors with bag-of-features model to represent the lesions. Since the melanoma and nevi share a large number of common patterns, the representation of original features are not sufficiently discriminative. Besides, the intrinsic relationship among different descriptors has also been ignored. In this paper, the representation of lesions is formulated as a multiview joint reconstruction problem with the learned dictionary. To better characterize the lesion images, a special_common dictionary learning method is proposed which explicitly takes both shared common pattern and a set of class-specific patterns between melanomas and nevi into account. In addition, group sparsity and locality constraints are further integrated to preserve the relationship and structure among local features. Furthermore, in order to enhance the combination capability of multiview, the spare coefficients of multiple features are jointly learned in a unified framework. The experimental results on both dermoscopic and conventional digital image databases demonstrate that our method is able to achieve better diagnosis accuracy than state-of-the-art ones.