1 / 16

Sequences II

Sequences II. Prof. Noah Snavely CS1114 http://cs1114.cs.cornell.edu. Administrivia. Assignments: A5P2 due on Friday Quiz Tuesday, 4/24 Final project proposals due on CMS by this Thursday. Google’s PageRank. H. Start at a random page, take a random walk. Where do we end up?. A. E. I.

byrd
Download Presentation

Sequences II

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Sequences II Prof. Noah Snavely CS1114 http://cs1114.cs.cornell.edu

  2. Administrivia • Assignments: • A5P2 due on Friday • Quiz Tuesday, 4/24 • Final project proposals due on CMS by this Thursday

  3. Google’s PageRank H Start at a random page, take a random walk. Where do we end up? A E I D B C F J G

  4. Google’s PageRank H Add 15% probability of moving to a random page. Now where do we end up? A E I D B C F J G

  5. Google’s PageRank H PageRank(P) = Probability that a long random walk ends at node P A E I D B C F J G

  6. Example (The ranks are an eigenvector of the transition matrix)

  7. Modeling Texture • What is texture? • An image obeying some statistical properties • Similar structures repeated over and over again • Often has some degree of randomness

  8. A D X B C • Higher order MRF’s have larger neighborhoods * * * * * * * * X * * * X * * * * * * * * * Markov Random Field • A Markov random field (MRF) • generalization of Markov chains to two or more dimensions. • First-order MRF: • probability that pixel X takes a certain value given the values of neighbors A, B, C, and D:

  9. Texture Synthesis [Efros & Leung, ICCV 99] • Can apply 2D version of text synthesis

  10. More Synthesis Results • Increasing window size

  11. More Results • aluminum wire • reptile skin

  12. Image-Based Text Synthesis

  13. Author recognition • Simple problem: Given two Markov chains, say Austen (A) and Dickens (D), and a string s (with n words), how do we decide whether A or D wrote s? • Idea: For both A and D, compute the probability that a random walk of length n generates s

  14. Probability of a sequence • What is the probability of a given n-lengthsequence s? s = s1 s2 s3 … sn • Probability of generating s = the product of transition probabilities: Probability that a sequence starts with s1 Transition probabilities

  15. Likelihood • Compute this probability for A and D Jane Austen wrote s “likelihood” of A Charles Dickens wrote s “likelihood” of D ???

  16. Problems with likelihood • Most strings of text (of significant length) have probability zero • Why? • Even if it’s not zero, it’s probably extremely small • What’s 0.01 * 0.01 * 0.01 * … (x200) … * 0.01? • According to Matlab, zero • How can we fix these problems?

More Related