Discriminant Manifold Learning via Sparse Coding for Image Analysis

Traditional subspace learning methods directly calculate the statistical properties of the original input images, while ignoring different contributions of different image components. In fact, the noise (e.g., illumination, shadow) in the image often has a negative influence on learning the desired subspace and should have little contribution to image recognition. To tackle this problem, we propose a novel subspace learning method named Discriminant Manifold Learning via Sparse Coding (DML_SC). In our method, we first decompose the input image into several components via dictionary learning, and then regroup the components into a More Important Part (MIP) and a Less Important Part (LIP). The MIP can be regarded as the clean part of the original image residing on a nonlinear submanifold, while LIP as noise in the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is able to deal with data with and without labels, yielding supervised and unsupervised DML SCs. Experimental results show that DML_SC achieves best performance on image recognition and clustering tasks compared with well-known subspace learning and sparse representation methods.