Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Epitomic analysis of appearance and shape
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Texture optimization for example-based synthesis
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2006 Papers
MUM '05 Proceedings of the 4th international conference on Mobile and ubiquitous multimedia
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH 2008 papers
Optimized scale-and-stretch for image resizing
ACM SIGGRAPH Asia 2008 papers
Multi-operator media retargeting
ACM SIGGRAPH 2009 papers
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Optimized image resizing using seam carving and scaling
ACM SIGGRAPH Asia 2009 papers
A system for retargeting of streaming video
ACM SIGGRAPH Asia 2009 papers
Shift-Map based stereo image retargeting with disparity adjustment
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Feedback retargeting combines the benefits of two previous retargeting methods: Bidirectional similarity [1] and Shift-Map [2]. The first method may have blurry areas due to patch averaging and the latter can remove entire objects. Feedback retargeting has the sharpness of shift-map and the completeness of bidirectional similarity, avoiding the removal of salient objects. In Shift-Map retargeting the output image is made from segments of the input image, and this minimizes the forward direction of bidirectional similarity. An iterative feedback procedure is developed to take care of the backward direction, assuring that the input image can be reconstructed from the output image. This is done by using Shift-Map backwards, reconstructing the input image back from the output image. Areas in the input image that are difficult to reconstruct from the output image get a feedback priority score. A second Shift-Map retargeting is then performed, adding this feedback priority to the data term. These regions now have a higher priority to be included in the output. After a few iterations of forward retargeting and backward feedback the retargeted image includes all salient features from the input image. Computational efficiency and image sharpness remain as high as in ordinary Shift-Map.