Video saliency detection based on random walk with restart

Jun Seong Kim, Hansang Kim, Jae Young Sim, Chang-Su Kim, Sang Uk Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A graph-based video saliency detection algorithm is proposed in this work. We model eye movements on an image plane as random walks on a graph. To detect the saliency of the first frame in a video sequence, we construct a fully connected graph, in which each node represents an image block. We assign an edge weight to be proportional to the dissimilarity between the incident nodes and inversely proportional to their geometrical distance. We extract the saliency level of each node from the stationary distribution of the random walker on the graph. Next, to detect the saliency of each subsequent frame, we add the criterion that an edge, connecting a slow motion node to a fast motion node, should have a large weight. We then compute the stationary distribution of the random walk with restart (RWR) simulation, in which the saliency of the previous frame is used as the restarting distribution. Experimental results show that the proposed algorithm provides more reliable and accurate saliency detection performance than conventional algorithms.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages2465-2469
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

Keywords

  • Markov chain
  • Saliency detection
  • random walk with restart
  • video saliency

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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