Improved Seam Carving for Video Retargeting Authors: Michael Rubinstein, Ariel Shamir and Shai...

Improved Seam Carving for Video Retargeting

Authors: Michael Rubinstein, Ariel Shamir and Shai Avidan Source: ACM Transactions on Graphics (TOG), Volume 27 Issue 3, August 2008Presenter: Hon-Hang ChangDate: 2011/ 09/ 30

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Outline• Introduction• Preliminaries• Seam Carving using Graph Cut• Forward Energy• Results• Conclusion

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To treat video as 3D cube

Introduction 1 D path on 2D image (Seam)

2D manifolds in a 3D volume

• In this paper the author define a new formulation of seam carving using graph cuts.

• Because of human perception, the temporal may even be more disturbing than spatial in video, as the human eye is highly sensitive to movement.

• 2D manifolds

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IntroductionA seam is a monotonic and connected path of pixels going from the top of the image to the bottom

Or from left to right

• The image size is reduced by one either in the horizontal or the vertical dimension.

• video should support retargeting capabilities as it is displayed on TVs, computers, cellular phones.

• A whole seam

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Preliminaries

Among this, {It} t=1~N

Extend the spatial L1-norm to a spatiotemporal L1-norm:

• Spatiotemporal L1-norm

Spatial term

Temporal term

Where,α ∈ [0, 1]

• Motion artifacts are more noticeable• Taking α= 0.3

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|}),(||),({|max),(1

jiIy

jiIx

jiE tt

N

tspatial

|}),({|max),(1

jiIt

jiE t

N

ttemporal

temporalspatialglobal EEjiE )1(),(

Seam Carving using Graph Cuts

S (Source)

T (Sink)

Source Sink

arc1

arc2

arc3

arc4

• Graph cut

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Seam Carving using Graph Cuts

An optimal seam must be:1.1.Valid

- A simple cut cannot define a seam carving2. Monotonic - Including only one pixel in each row (or column)3. Connected -The seam must be connected

• The proposed formulation extend seam carving to video and define a monotonic and connected 2D manifold seam inside the video cube.

• Optimal seam in graph cut

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Seam Carving using Graph Cuts• Graph cut for image (Non-monotonic)

• Every internal node P is connected to its four neighbors Nbr(Pi,j) = {Pi−1,j , Pi+1,j , Pi,j−1, Pi,j+1}.

Pi,j

Pi-1,j

Pi,j-1 Pi,j+1

Pi+1,j

Neighbors pixels

∂x(i, j) = |I(i, j + 1) − I(i, j)|

∂y(i, j) = |I(i+1, j ) − I(i, j)|

Horizontal direction

Vertical direction

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Seam Carving using Graph Cuts• Graph cut for image

(Unconnected)

• Prove_1:

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Seam Carving using Graph Cuts

E1(i, j) =∂x(i, j) + ∂y(i, j)

• Graph cut for image (Original)

• Prove_2:

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Seam Carving using Graph Cuts• Graph cut for image

• We can use any energy function defined on the pixels as the weight of the forward horizontal arcs .

• Achieve the same results as the original dynamic programming based seam carving

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Seam Carving using Graph Cuts• Graph cut for video

• The extension to video is straightforward. 12

Seam Carving using Graph Cuts• Graph cut for video

• Computation time is quadratic in the number of voxels. O(mn2)- n #of nodes ; m#of arcs

• Performance issues are encountered already for high resolution images

• The graph cut approach to seam carving allows us to extend the benefits of content-aware resizing to video. Still, the method is not perfect and no single energy function was shown to perform properly in all cases. Therefore, they introduce a new energy function that better protects media content, and improves video results.

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Forward Energy

• Remove the seam with the least amount of energy from the image

• The inserted energy is due to new edges created by previously non adjacent pixels that become neighbors once the seam is removed

• To measure the real change in energy after a removal of a seam

They measure the difference in the energy of the image after the removal (It=i+1) and the energy of only those parts that were not removed in the previous image

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Forward Energy

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+LR = | I(i, j+1)− I(i, j−1)| (arc weight between pi,j and pi,j+1)difference between the Left and Right neighbors

+LU = | I(i-1, j)− I(i, j−1)| (arc weight between pi,j and pi-1,j)difference between the Left and Up neighbors

-LU = | I(i+1, j)− I(i, j−1)| (arc weight between pi,j and pi+1,j)difference between the Left and Up neighbors with respect to the end point of the arrow

Forward Energy• Forward Energy in Graph Cut

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Forward Energy• Forward Energy in Graph Cut

+LR = | I(i, j+1)− I(i, j−1)| (arc weight between pi,j and pi,j+1)difference between the Left and Right neighbors

+LU = | I(i-1, j)− I(i, j−1)| (arc weight between pi,j and pi-1,j)difference between the Left and Up neighbors

-LU = | I(i+1, j)− I(i, j−1)| (arc weight between pi,j and pi+1,j)difference between the Left and Up neighbors with respect to the end point of the arrow 17

Results

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Results

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Conclusion

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Video retargeting is achieved using graph cuts and we have shown a construction that is consistent with the dynamic programming approach.