BiggerPicture: data-driven image extrapolation using graph matching
Miao Wang, Yu-Kun Lai, Yuan Liang, Ralph R. Martin, Shi-Min Hu
In ACM Transactions on Graphics, 33(6), November 2014.
Abstract: 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.
Article URL: http://dx.doi.org/10.1145/2661229.2661278
BibTeX format:
@article{Wang:2014:BDI,
  author = {Miao Wang and Yu-Kun Lai and Yuan Liang and Ralph R. Martin and Shi-Min Hu},
  title = {BiggerPicture: data-driven image extrapolation using graph matching},
  journal = {ACM Transactions on Graphics},
  volume = {33},
  number = {6},
  pages = {173:1--173:13},
  month = nov,
  year = {2014},
}
Search for more articles by Miao Wang.
Search for more articles by Yu-Kun Lai.
Search for more articles by Yuan Liang.
Search for more articles by Ralph R. Martin.
Search for more articles by Shi-Min Hu.

Return to the search page.


graphbib: Powered by "bibsql" and "SQLite3."