Superpixel-based spatiotemporal saliency detection

  • Liu Z
  • Zhang X
  • Luo S
 et al. 
  • 37


    Mendeley users who have this article in their library.
  • 71


    Citations of this article.


This paper proposes a superpixel-based spatiotemporal saliency model for saliency detection in videos. Based on the superpixel representation of video frames, motion histograms and color histograms are extracted at the superpixel level as local features and frame level as global features. Then, superpixel-level temporal saliency is measured by integrating motion distinctiveness of superpixels with a scheme of temporal saliency prediction and adjustment, and superpixel-level spatial saliency is measured by evaluating global contrast and spatial sparsity of superpixels. Finally, a pixel-level saliency derivation method is used to generate pixel-level temporal and spatial saliency maps, and an adaptive fusion method is exploited to integrate them into the spatiotemporal saliency map. Experimental results on two public datasets demonstrate that the proposed model outperforms six state-of-the-art spatiotemporal saliency models in terms of both saliency detection and human fixation prediction.

Author-supplied keywords

  • Spatiotemporal saliency detection
  • Superpixel
  • motion vector field
  • spatial saliency
  • temporal saliency

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free