change blindness images
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Change Blindness Images
Li-Qian Ma1, Kun Xu1, Tien-Tsin Wong2, Bi-Ye Jiang1, Shi-Min Hu1
1Tsinghua University2The Chinese University of Hong Kong
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
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
𝑆 ( 𝐼𝑘 )=𝑆0 ( 𝐼𝑘 )⋅𝐶 (𝐼𝑘)
Existing saliency model
Spatially varying complexity
Context-dependent saliency
Color Similarity
• Color similarity :
𝑒𝑖𝑗=exp (−𝐷𝑐
2 ( 𝐼 𝑖 , 𝐼 𝑗 )𝜎𝑒
2 )
𝐼 𝑖 𝐼 𝑗 𝐼 𝑖 𝐼 𝑗
Small color similarity Large color similarity
Spatial varying Complexity
• Weighted sum of color similarities between all region pairs around𝐶 ( 𝐼𝑘 )=∑
𝑖 , 𝑗
𝜔 𝑖𝑗𝑒𝑖𝑗/∑𝑖 , 𝑗
𝜔𝑖𝑗
Spatial varying Complexity
𝑤𝑖𝑗=|𝐼𝑖||𝐼 𝑗|exp (− (𝑐𝑖−𝑐 𝑗 )2
𝜎𝑤2 )exp (− (𝑐 𝑖−𝑐𝑘 )2+(𝑐 𝑗−𝑐𝑘 )2
𝜎𝑤2 )
𝐼 𝑖 𝐼 𝑗
𝐶 ( 𝐼𝑘 )=∑𝑖 , 𝑗
𝜔 𝑖𝑗𝑒𝑖𝑗/∑𝑖 , 𝑗
𝜔𝑖𝑗
𝐼𝑘
Context-Dependent Saliency
Input images Global contrast saliency
Spatial varying complexity
Context-dependent saliency
Context-Dependent Saliency
Input image Global contrast saliency Learning-based saliency Image signature
Itti model AIM saliency Judd model Context-Dependent Saliency
Synthesis
Original Image Changed Counterpart
Desired Difficulty = 0.5
1. Randomly pick a region and a change operator 2. Search in the parameter space of the change operator
Move
Measured Difficulty B =1 0.70.5
More Results
Original Image Changed Counterpart
Desired Difficulty = 0.2 Desired Difficulty = 0.5 Desired Difficulty = 0.8
User Study
Model Global contrast
Learning based
Image signature
Ittimodel
Correlation 0.44 0.38 0.34 0.42
Model Judd model
AIMmodel
Context-Dependent
Correlation 0.43 0.42 0.74
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)