Object Level Saliency by Submodular Optimization

Saliency detection is an important task to detect humans' visual attention region, plenty of methods are proposed to solve it. Most of them use single level pixels or patches to reveal the structure of the given image. However, the complexity of background regions and the unevenness of foreground objects still challenge these methods. To distinguish the saliency object from the complex background region and guarantee the uniformity of patches in the same object. A novel saliency detection method based sub modular optimization is proposed in this paper. Firstly we propose an image structure with both mid-level super pixels and object-level regions. The object-level regions are created by optimal sub modular clustering. Secondly, we use spatial based color contrast method to calculate the raw saliency map. Then the raw saliency is adjusted by three priors. The priors can reduce the impact of complex background regions and help generating the mid-level saliency map. Finally, the sub modular optimization method is used to jointly select the diffusion seeds and diffuse the mid-level saliency map into object-level saliency map.