Salient Object Detection via Trace Representation and Regularization

Salient object detection intends to identify salient areas in natural images.According to low rank recovery theory,we propose a method via trace representation and regularization for salient object detection to separate the salient areas of the image from the background more completely.Firstly,a trace representation of matrix is used to obtain lower rank solution rather than the nuclear norm.Secondly,a Laplacian regularization is merged into model to reduce connection between sparse matrix and low-rank matrix.Finally,the color,location and boundary connectivity priors are integrated into a weight matrix,which is incorporated into the matrix decomposition model.Comparing with thirteen state-of-the-art methods in four challenging databases:MSRA1K,SOD,ECSSD and iCoseg,the experimental results based on Matlab show that our approach outperforms the state-of-the-art methods.