Saliency detection based on integration of central bias, reweighting and multi-scale for superpixels

Xiaoling Hu; Wenming Yang; Fei Zhou; Qingmin Liao

ICASSP 2016accepted0 citations

Saliency detection based on integration of central bias, reweighting and multi-scale for superpixels

Xiaoling Hu, Wenming Yang, Fei Zhou, Qingmin Liao

Abstract

Saliency detection has been a significant problem in computer vision and helpful to object detection. In this paper, we propose a new computational saliency detection model under the Bayesian framework. First, central bias and the reweighting of the salient regions in the convex hull are applied to guide the prior map. Then, multi-scale for superpixels is proposed to detect objects with various scales. At last, the Bayes formula is adopted to obtain the final saliency map. Experimental results on a standard database show that the proposed model outperforms state-of-the-art methods.

BibTeX

@inproceedings{icassp2016_saliencydetectio,
  title = {Saliency detection based on integration of central bias, reweighting and multi-scale for superpixels},
  author = {Xiaoling Hu and Wenming Yang and Fei Zhou and Qingmin Liao},
  booktitle = {ICASSP 2016},
  year = {2016}
}