Change Blindness Images

Li-Qian Ma1, Kun Xu1, Tien-Tsin Wong2, Bi-Ye Jiang1, Shi-Min Hu1
1Tsinghua University
2The Chinese University of Hong Kong

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.

Paper: [PDF 4.0M].
Presentation: [PPTX 6.9M]
Supplemental Document: [PDF 3.6M].
Supplemental Video: [MP4 13.6M].

Dataset: [ZIP 97.8M].

Bibtex: @article{Ma13Tvcg,
author = {Li-Qian Ma and Kun Xu and Tien-Tsin Wong and Bi-Ye Jiang and Shi-Min Hu},
title = {Change Blindness Images},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {19},
number = {11},
year = {2013},
pages = {1808--1819},