Robust image representation and decomposition by Laplacian regularized latent low-rank representation

Abstract

This paper discusses the image representation and decomposition problem using enhanced low-rank representation. Technically, we propose a Regularized Low-Rank Representation framework referred to as rLRR that is motivated by the fact that Latent Low-Rank Representation (LatLRR) delivers robust and promising results for image representation and feature extraction through recovering the hidden effects, but the locality among both similar principal and salient features to be encoded are not preserved in the original LatLRR formulation. To address this problem for obtaining enhanced performance, rLRR is proposed through incorporating an appropriate Laplacian regularization term that allows us to keep the local geometry of close features. Similar to LatLRR, rLRR decomposes a given data matrix from two directions by calculating a pair of low-rank matrices. But the similarities among principal features and salient features can be clearly preserved by rLRR. Thus the correlated features can be well grouped and the robustness of representations can also be effectively improved. The effectiveness of rLRR is examined by representation and recognition of real images. Results verified the validity of our presented rLRR technique.