Learning to Detect A Salient Object
Top Cited Papers
- 1 June 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 1-8
- https://doi.org/10.1109/cvpr.2007.383047
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, center-surround 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.Keywords
This publication has 15 references indexed in Scilit:
- Convergent Tree-Reweighted Message Passing for Energy MinimizationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
- An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection SpeedPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Region Enhanced Scale-Invariant Saliency DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Gaze-based interaction for semi-automatic photo croppingPublished by Association for Computing Machinery (ACM) ,2006
- A coherent computational approach to model bottom-up visual attentionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Integral histogram: a fast way to extract histograms in Cartesian spacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Is bottom-up attention useful for object recognition?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Learning to detect natural image boundaries using local brightness, color, and texture cuesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Contrast-based image attention analysis by using fuzzy growingPublished by Association for Computing Machinery (ACM) ,2003
- A feature-integration theory of attentionCognitive Psychology, 1980