BiggerPicture: Data-driven Image Extrapolation Using Graph Matching

Miao Wang1     Yu-Kun Lai2     Yuan Liang1     Ralph R. Martin2     Shi-Min Hu1

1 TNList, Tsinghua University, Beijing

2 Cardiff University


Filling a small hole in an image with plausible content is well studied. Extrapolating an image to give a distinctly larger one is much more challenging---a significant amount of additional content is needed which matches the original image, especially near its boundaries. We propose a data-driven approach to this problem. Given a source image, and the amount and direction(s) in which it is to be extrapolated, our system determines visually consistent content for the extrapolated regions using library images. As well as considering low-level matching, we achieve consistency at a higher level by using graph proxies for regions of source and library images. Treating images as graphs allows us to find candidates for image extrapolation in a feasible time. Consistency of subgraphs in source and library images is used to find good candidates for the additional content; these are then further filtered. Region boundary curves are aligned to ensure consistency where image parts are joined using a photomontage method. We demonstrate the power of our method in image editing applications.



Snapshot for paper BiggerPicture: Data-driven Image Extrapolation Using Graph Matching

Miao Wang, Yu-Kun Lai, Yuan Liang, Ralph R. Martin and Shi-Min Hu

ACM Transactions on Graphics, vol. 33(6), SIGGRAPH Asia 2014 (to appear).

paper [Pre-print 43MB][BibTex]
data [Supplementary document 40MB][Full set of results 488MB]



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