Abstract We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, centersurround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A Conditional Random Field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper.
Liu et al. REF considered saliency object detection as a binary labeling task that separates the salient object from the background.
Learning to Detect A Salient Object
2007 IEEE Conference on Computer Vision and Pattern Recognition
2007 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science