Saliency Optimization from Robust Background Detection

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Presentation transcript:

Saliency Optimization from Robust Background Detection COMPUTER VISION LAB. 여동훈

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

Detect background boundary superpixel  

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

   

Bondary Connectivity of a superpixel

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

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

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

Background Weighted Contrast Contrast Saliency Background Weighted Contrast    

Comparison on two algorithms Contrast Saliency Background weighted Contrast

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

Saliency Optimization      

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

Experiments BndCon Ctr wCtr wCtr*

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

Optimization on Several Algorithms

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