1. Saliency Optimization from Robust Background Detection
COMPUTER VISION LAB.
여동훈

2. Main Steps (a) Detect background boundary superpixel
(b) Compute Distance from background
(c) Saliency Optimization from (b)
(a)
(b)
(c)

3. Detect background boundary superpixel

4. Without segmentation – soft segmentation
Two terms
Area of a region R
Length along the boundary
Compute the boundary connectivity for each superpixel
The boundary connectivity of a soft region containing the superpixel

6. Bondary Connectivity of a superpixel

7. Boundary Connectivity Values on images
(b) Edges link adjacent superpixels only
(c) Edges additionally connect all pairs of boundary superpixels

8. Experimental Results on Boundary connectivity
Thresholding Boundary connectivity with a single threshold (2)
The values of the boundary connectivity is stable over the images

9. Main Steps (a) Detect background boundary superpixel
(b) Compute Distance from background
(c) Saliency Optimization from (b)
(a)
(b)
(c)

10. Background Weighted Contrast
Contrast Saliency
Background Weighted Contrast

11. Comparison on two algorithms
Contrast Saliency
Background weighted Contrast

12. Main Steps (a) Detect background boundary superpixel
(b) Compute Distance from background
(c) Saliency Optimization from (b)
(a)
(b)
(c)

13. Saliency Optimization

14. Measurement Precision – Recall curve Mean absolute error (MAE)
Average per-pixel difference gt
ours AE

15. Experiments
BndCon
Ctr
wCtr
wCtr*

16. MSRA-hard dataset
657 images where salient object touches image boundary

17. Optimization on Several Algorithms