abdulhakeem
21-08-07, 03:48 AM
Scene Completion Using Millions of Photographs
People
James Hays
Alexei Efros
Abstract
What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data-driven, requiring no annotations or labelling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of image completions and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches.
Paper
scene-completion.pdf, 11MB
Presentation
.zip file (ppt + movies), 90MB
Citation
James Hays, Alexei A. Efros. Scene Completion Using Millions of Photographs. ACM Transactions on Graphics (SIGGRAPH 2007). August 2007, vol. 26, No. 3.
Bibtex
Data
Test set containing 51 image/mask pairs used for our user study.
Additional test cases. 27 more pairs.
Funding
This research is supported in part by:
NSF Graduate Research Fellowship to James Hays.
NSF grant CAREER IIS-0546547.
NSF grant CCF-0541230.
http://graphics.cs.cmu.edu/projects/scene-completion/
People
James Hays
Alexei Efros
Abstract
What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data-driven, requiring no annotations or labelling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of image completions and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches.
Paper
scene-completion.pdf, 11MB
Presentation
.zip file (ppt + movies), 90MB
Citation
James Hays, Alexei A. Efros. Scene Completion Using Millions of Photographs. ACM Transactions on Graphics (SIGGRAPH 2007). August 2007, vol. 26, No. 3.
Bibtex
Data
Test set containing 51 image/mask pairs used for our user study.
Additional test cases. 27 more pairs.
Funding
This research is supported in part by:
NSF Graduate Research Fellowship to James Hays.
NSF grant CAREER IIS-0546547.
NSF grant CCF-0541230.
http://graphics.cs.cmu.edu/projects/scene-completion/