Change Blindness Images Li-Qian Ma 1, Kun Xu 1, Tien-Tsin Wong 2, Bi-Ye Jiang 1, Shi-Min Hu 1 1 Tsinghua University 2 The Chinese University of Hong Kong

Spot-the-difference Game

Motivation These image pairs are mainly generated by artists manually The degree of recognition difficulty is controlled by artists empirically

Goal Given an image, automatically generate a counterpart of the image With a controlled degree of “difficulty”

Psychological background Change blindness –Widely studied in psychology is caused by failure to store visual information in our short-term memory –Factors influencing visual attention (saliency), object presentation –Mostly qualitative

The Metric We define a metric to measure the blindness of an image pair There is a single change between the image pair The change region and the operator are known in advance The change is limited to the following operators: –Insertion/Deletion –Replacement –Relocation –Scaling –Rotation –Color-shift

The Metric

Amount of Change Color Difference Texture Difference Spatial Difference

Saliency Visual attention is highly context-dependent No existing saliency model attempts to explicitly quantify background complexity

Context-Dependent Saliency Modulate saliency via spatially varying complexity Existing saliency model Spatially varying complexity Context-dependent saliency

Color Similarity Color similarity : Small color similarityLarge color similarity

Spatial varying Complexity Weighted sum of color similarities between all region pairs around

Spatial varying Complexity

Context-Dependent Saliency Input images Global contrast saliency Spatial varying complexity Context-dependent saliency

Context-Dependent Saliency Input image Global contrast saliencyLearning-based saliencyImage signature Itti modelAIM saliencyJudd modelContext-Dependent Saliency

Synthesis Optional user manually refinement Original Image

Synthesis Original ImageChanged Counterpart 1.Randomly pick a region and a change operator 2.Search in the parameter space of the change operator Move Measured Difficulty B =

More Results

Original Image Changed Counterpart

More Results

User Study Generate 100 image pairs 30 subjects Pearson’s correlation: 0.74

User Study ModelGlobal contrast Learning based Image signature Itti model Correlation ModelJudd model AIM model Context- Dependent Correlation

Conclusion Computational model for change blindness Context-dependent saliency model Change blindness image synthesis with desired degree of blindness

Future Works Add high-level image features into the metric Improve the predictability using more sophisticated forms Improve the accuracy of the metric considering just-noticeable difference(JND)

Acknowledgement Anonymous TVCG reviewers Thank you for your attention.