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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. Spot-the-difference Game. Motivation. These image pairs are mainly generated by artists manually

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Presentation Transcript
slide1
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

motivation
Motivation
  • These image pairs are mainly generated by artists manually
  • The degree of recognition difficulty is controlled by artists empirically
slide5
Goal
  • Given an image, automatically generate a counterpart of the image

With a controlled degree of “difficulty”

psychological background
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
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 metric1
The Metric

: the amount of changes

amount of change
Amount of Change

Color Difference

Spatial Difference

Texture Difference

saliency
Saliency
  • Visual attention is highly context-dependent
  • No existing saliency model attempts to explicitly quantify background complexity
context dependent saliency
Context-Dependent Saliency
  • Modulate saliency via spatially varying complexity

Spatially varying complexity

Context-dependent saliency

Existing saliency model

color similarity
Color Similarity
  • Color similarity :

Small color similarity

Large color similarity

spatial varying complexity
Spatial varying Complexity
  • Weighted sum of color similarities between all region pairs around
context dependent saliency1
Context-Dependent Saliency

Global contrast

saliency

Context-dependent

saliency

Spatial varying

complexity

Input images

context dependent saliency2
Context-Dependent Saliency

Input image

Global contrast saliency

Learning-based saliency

Image signature

Itti model

AIM saliency

Judd model

Context-Dependent Saliency

synthesis
Synthesis

Original Image

  • Optional user manually refinement

Desired Difficulty = 0.5

synthesis1
Synthesis

Changed Counterpart

Original Image

Move

Desired Difficulty = 0.5

Measured Difficulty B =

0.5

0.7

1

  • Randomly pick a region and a change operator
  • Search in the parameter space of the change operator
more results2
More Results

Changed Counterpart

Original Image

Desired Difficulty = 0.2

Desired Difficulty = 0.5

Desired Difficulty = 0.8

user study
User Study
  • Generate 100 image pairs
  • 30 subjects
  • Pearson’s correlation:

0.74

conclusion
Conclusion
  • Computational model for change blindness
  • Context-dependent saliency model
  • Change blindness image synthesis with desired degree of blindness
future works
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
Acknowledgement
  • Anonymous TVCG reviewers

Thank you for your attention.