Sparse Gradient Based Structured Matrix Decomposition for Salient Object Detection

Abstract

In the salient object detection, the given image can be decomposed into background regions (low-rank part) and salient regions (sparse part). In this paper, we present a novel sparse gradient-based structured matrix decomposition model for salient object detection. We use the ${l_{1}}$ norm of logistic function on the singular values to approximate the rank function, which avoid over-penalized problem of the nuclear norm. And a group sparsity induced norm regularization is imposed on the salient part to explore the relationship among superpixels. In order to widen the gap between salient regions and background regions in feature space, we suggest a sparse gradient regularization to replace the conventional Laplacian regularization. Finally, the model is solved through an augmented Lagrange multipliers method, and high-level priors are embedded into our model to promote the performance. Experiments indicate that the proposed method performs better in terms of various evaluation metrics than the state-of-the-art methods.