A novel position prior using fusion of rule of thirds and image center for salient object detection

Salient object detection is one of the challenging problems in the field of computer vision. Most of the models use a center prior to detect salient objects. They give more weightage to the objects which are present near the center of the image and less weightage to the ones near the corners of the image. But there may be images in which object is placed near the image corner. In order to handle such situation, we propose a position prior based on the combined effect of the rule of thirds and the image center. In this paper, we first segment the image into an optimal number of clusters using Davies-Bouldin index. Then the pixels in these clusters are used as samples to build the Gaussian mixture model whose parameters are refined using Expectation-Maximization algorithm. Thereafter the spatial saliency of the clusters is computed based on the proposed position prior and then combined into a saliency map. The performance is evaluated both qualitatively and quantitatively on six publicly available datasets. Experimental results demonstrate that the proposed model outperforms the seventeen existing state-of-the-art methods in terms of F -measure and area under curve on all the six datasets.