Image edge preservation via low-rank residuals for robust subspace learning

In order to maintain low-rank characteristics, existing low-rank representation methods concentrate on capturing data's low-frequency signals, which are presumed to be the global data structure, and they delete the high ones, which are often a combination of corrupt elements and image edges. Such inefficient preservation of image edges could hamper discriminative details in images, especially in heavy corruptions. This paper proposes a new method, which preserves image edges by finding robust subspace projections from low-rank residuals. It is achieved through a least square minimization of the discrepancy between similar residuals in a manifold learning framework. Edge preserved subspace projections are learned from such residuals by reducing the influence of corrupt ones using a dynamic affinity graph regularization. Furthermore, through our adaptive learning approach, the proposed method can jointly find image intrinsic low-rank representation. Several experimental results in classification and clustering tasks demonstrate the proposed method's effectiveness over state-of-the-art (SOTA) methods.