ML Anthology

Saliency Detection: A Spectral Residual Approach

Xiaodi Hou, Liqing Zhang
CVPR 2007
doi:10.1109/CVPR.2007.383267 /cvpr/2007/hou2007cvpr-saliency/

Abstract

The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method.

CVPR Semantic Scholar

Cite

Text

Hou and Zhang. "Saliency Detection: A Spectral Residual Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383267

Markdown

[Hou and Zhang. "Saliency Detection: A Spectral Residual Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/hou2007cvpr-saliency/) doi:10.1109/CVPR.2007.383267

BibTeX

@inproceedings{hou2007cvpr-saliency,
  title     = {{Saliency Detection: A Spectral Residual Approach}},
  author    = {Hou, Xiaodi and Zhang, Liqing},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383267},
  url       = {https://mlanthology.org/cvpr/2007/hou2007cvpr-saliency/}
}