ICASSP 2016accepted0 citations

Saliency & structure preserving multi-operator image retargeting

Lingling Zhu, Zhibo Chen, Xiaoming Chen, Ning Liao

Abstract

Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can preserve important image contents and structure well without introducing deformation. To address this problem, we propose a Saliency & Structure Preserving Multi-operator (SSPM) method. SSPM classifies images into three categories utilizing SIFT density to improve performance of saliency preservation, helping to mitigate negative influence from center-bias property of most existing saliency detection models. SSPM also employs different principles to improve structure preservation performance, including Earth Mover's Distance (EMD) and Gray-Level Cooccurrence Matrix (GLCM) to get optimal operator sequences for smart content-aware image retargeting. SSPM method not only can well preserve salient contents and structure, but also can greatly improve deformation resilience. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.

BibTeX
@inproceedings{icassp2016_saliencystructur,
  title = {Saliency & structure preserving multi-operator image retargeting},
  author = {Lingling Zhu and Zhibo Chen and Xiaoming Chen and Ning Liao},
  booktitle = {ICASSP 2016},
  year = {2016}
}