In the field of saliency detection, many graph-based algorithms use boundary pixels as background seeds to estimate the background and foreground saliency,which leads to significant errors in some of pictures. In addition, local context with high contrast will mislead the algorithms. In this paper, we propose a novel multilevel bottom-up saliency detection approach that accurately utilizes the boundary information and takes advantage of both region-based features and local image details. To provide more accurate saliency estimations, we build a three-level graph model to capture both region-based features and local image details. By using superpixels of all four boundaries, we first roughly figure out the foreground superpixels. After calculating the RGB distances between the average of foreground superpixels and every boundary superpixel, we discard the boundary superpixels with the longest distance to get a set of accurate background boundary queries. Finally, we propose the regularized random walks ranking to formulate pixel-wise saliency maps. Experiment results on two public datasets indicate the significantly promoted accuracy and robustness of our proposed algorithm in comparison with 7 state-of-the-art saliency detection approaches.
This is a matlab implemention of the saliency detection algorithm proposed in the paper.
Run main.m file to compare the result between my algorithm and the result from this paper
In the field of saliency detection, many graph-based algorithms use boundary pixels as background seeds to estimate the background and foreground saliency,which leads to significant errors in some of pictures. In addition, local context with high contrast will mislead the algorithms. In this paper, we propose a novel multilevel bottom-up saliency detection approach that accurately utilizes the boundary information and takes advantage of both region-based features and local image details. To provide more accurate saliency estimations, we build a three-level graph model to capture both region-based features and local image details. By using superpixels of all four boundaries, we first roughly figure out the foreground superpixels. After calculating the RGB distances between the average of foreground superpixels and every boundary superpixel, we discard the boundary superpixels with the longest distance to get a set of accurate background boundary queries. Finally, we propose the regularized random walks ranking to formulate pixel-wise saliency maps. Experiment results on two public datasets indicate the significantly promoted accuracy and robustness of our proposed algorithm in comparison with 7 state-of-the-art saliency detection approaches.