1. Texture Recognition and Synthesis A Non-parametric Multi-Scale Statistical Model by De Bonet & Viola Artificial Intelligence Lab MIT Presentation by Pooja

2. Main Goal 1.Train on example images 2.Recognize novel images 3.Generate new images How?

3. Markov Random Fields (MRFs) Based on simple, local interactions Success in restoration Weak generative properties –Inability to capture long range interactions

4. Wavelet Transform Effective for modeling natural images Measures the underlying causes of images –Assumption: causes are statistically independent Coefficients are uncorrelated

5. Multi-scale Wavelet Techniques Iterative convolution of bank of filters –Pyramid of low frequency downsampled images Images are a linear transform of statistically independent causes

6. Texture Synthesis Bergen and Heeger –Inverse wavelet transform

7. Other synthesis failures Find me!

8. Other synthesis failures (contd) Find me!

9. Other synthesis failures (contd) Hehehe, find me this time!!

10. Think Think Think! Why/When does synthesis fail? What does it tell us about requirements in a successful synthesis technique?

11. Objective of Synthesis Different from the original Generated by the same underlying stochastic process

12. Back to Texture Recognition Gaurav’ll do all the synthesis explaining (Thank God!)

13. Wavelet coefficients not independent Long edges? Parent vector of a pixel defined as

14. Probabilistic model Generation of nearby pixels strongly dependent

15. Conditional Distributions Estimated as a ratio of Parzen window density estimators :

16. Cross Entropy (Motivation) Biased coin: p(h) = 0.75 1st output: #h = 75, #t = 25 2 nd output: #h = 100, #t = 0 Which is more likely? Which is more typical? Concept of cross entropy (Kullback-Liebler divergence)

17. Cross Entropy Viewed as the difference between two expected log likelihoods Replace integral with monte-carlo sampling Lowest cross entropy vs. false positives (or negatives)?

18. Results Standardized tests –“easy” data sets – Brodatz texture test suite –100% correct classification Natural textures –20 types of natural texture –87% correct classification (humans: 93%)

19. Pros & Cons Pros –Pyramidal dependency –Concept of likely vs. typical –False positives vs. overall low cross entropy Cons –Estimation of conditional distributions? –The R(.) function? –Works well on simple texture sets! So? –Only 20 natural textures?? –Errors in the paper 

20. And finally…. A not so boring slide! A not so boring slide! Do you think their method would work on this texture?? Do you think their method would work on this texture??