In this paper, we show that the seminal, biologically inspired saliency model by Itti et at. {21] is still compet itive with current state-of-the-art methods for salient ob ject segmentation if some important adaptions are made. We show which changes are necessary to achieve high performance, with special emphasis on the scale-space: we introduce a twin pyramid for computing Dif f erence-of Gaussians, which enables a flexible center-surround ratio. The resulting system, called VOCUS2, is elegant and co herent in structure, fast, and computes saliency at the pixel level. It is not only suitable for images with few objects, but also for complex scenes as captured by mobile devices. Fur thermore, we integrate the saliency system into an object proposal generation framework to obtain segment-based saliency maps and boost the results for salient object seg mentation. We show that our system achieves state-of-the art peiformance on a large collection of benchmark data.
First, the saliency system VOCUS2 REF generates a saliency map.
Traditional saliency reloaded: A good old model in new shape
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science