Data-Driven is on the way!

  • Flickr: > 1.7 million photos / day
  • Facebook: > 30 million photos / day
  • YouTube: > 20 hours of video every minute

Exploiting online photo collections – large and keep-growing data set!

1. Organizing and visualizing photos and scenes

  • Photo Tourism – S. Seitz, R. Szeliski
  • Towards Internet-scale Multi-view Stereo, Furukawa, et al., CVPR 2010
  • PhotoSynth
  • Exploring virtual spaces – Creating and exploring a large photorealistic virtual space, Sivic et al., Workshop on Internet Vision 2008.

2. Image enhencement

  • Scene Completion using Millions of Photographs,Hays and Efros, SIGGRAPH 2007, with Gist scene descriptor (Oliva and Torralba 2001)
  • Photo Clip Art, Lalonde et al., SIGGRAPH 2007
  • Intrinsic Colorization, Liu et al., SIGGRAPH Asia 2008
  • Sketch2Photo: Internet Image Montage, Chen et al., SIGGRAPH Asia 2009
  • Deep Photo: Model-Based Photograph Enhancement and Viewing, Kopf et al., SIGGRAPH Asia 2008
  • Bing Maps 3D

3. Understanding people, places, and cameras

  • Mapping the World’s Photos, Crandall et al., WWW 2009
  • Priors for Large Photo Collections and What They Reveal about Cameras, S. Kuthirummal, A. Agarwala, D. B Goldman, and S. K. Nayar, ECCV 2008

4. How large is “sufficiently large”?

“Indeed, our initial experiments with the gist descriptor on a dataset of ten thousand images were very discouraging. However, increasing the image collection to two million yielded a qualitative leap in performance…”

-80 million tiny images: a large dataset for non-parametric object and scene recognition, A. Torralba, R. Fergus, W. T. Freeman, PAMI 2008
5. “Brute-force Image Understanding”

“Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library.” Photo Clip Art, Lalonde et al., SIGGRAPH 2007

6. Law of Large Image Collections

“Given a sufficiently large corpus of images, with high probability there exists an image similar to the one you are looking for.”

  1. Vision algorithms are starting to be robust enough to work on large-scale Internet photo collections
  2. Law of large photo collections can make some hard problems easier
  3. Statistics of large photo collections reveal useful information about people, places, and cameras

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