Change blindness images

Li-Qian Ma, Kun Xu, Tien-Tsin Wong, Bi-Ye Jiang, Shi-Min Hu

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)

Abstract

Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.

Original languageEnglish
Pages (from-to)1808-1819
Number of pages12
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number11
DOIs
Publication statusPublished - Nov 2013
Externally publishedYes

Keywords

  • Change blindness
  • image synthesis

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