Learning to Detect A Salient Object

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.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Tie Liu, Jian Sun, Nanning Zheng, Xiaoou Tang, Heung-Yeung Shum
• Keywords: Computer Vision, CVPR 2007, Image Segmentation Problem, Input Image, Large Image Database, Salient Object Detection, Visual Attention Algorithms