1. A Compression Based Distance Measure for Texture Bilson J. L. Campana Eamonn J. Keogh University of California – Riverside bcampana@cs.ucr.edu 1

2. Outline of the Talk What makes texture important? Why is texture hard to mine? The CK method and CK-1 measure. Rival methods. Datasets and experimenting. The results. 2

3. exture is Everywhere!! exture is Everywhere!! 3

4. But what IS texture? The old forget, the young don’t know! Global scalars Entropy Standard Deviation Energy … Global vectors Wavelet coefficients Fourier Transforms … Local Features SIFT descriptors Textons … 4

5. Mining Textures Textures are ubiquitous in images Proper analysis of an image should take into account many details – Texture – Color – Shape – Geospatial data – Etc. Current approaches for texture analysis require far to much tuning – Cannot simply use texture algorithms correctly for many datasets There seems to be texture, but I don’t want to spend the time setting up and tuning if it doesn’t work! We’ve formed a simple solution to your problems! 5

6. The CK Method Everything should be made as simple as possible, but not simpler. -Albert Einstein Simple things are easily understood, accepted and used. Measure image similarity by exploiting video compression 6

7. Kolmogorov Complexity abababababababababababababababab b4w1x8nb2y39abgk5q85s7arjqj0cvab The Kolmogorov complexity K(x) of a string x is a measure of the resources needed to specify x Consider this example… 7 And now, conditional complexity K(x|y)…

8. How MPEG1 Works Three types of frames I, B, P Encoder settings are intuitively set and empirically tested IBP 8 In this example, the P frame has 1 reference to the I frame.

9. x Query images are used to create a two frame movie. y C(x|y) y x C(y|x) x x C(x|x) y y C(y|y) - 1 You can’t control what you can’t measure. -Tom DeMarco The CK-1 Measure 9

10. Apply Invariance to Rotation As you’ll see. CK-1 is very FAST! So you can just measure two images several times while rotating them? PRECISELY! 10

11. Rival Algorithms Gabor Filter Banks* – Widely used for its ability to be tuned to many applications – Six orientations and four scales – Filters are convoluted through the image and responses are gathered into a response vector Textons** – Classification from clustered filter responses – Extended from the previous filter bank implementation *P. Wu, B. S. Manjunath, S. Newsam, H. D. Shin, A texture descriptor for browsing and similarity retrieval, Signal Processing: Image Communication, Volume 16, Issues 1-2, Pages 33-43, September 2000. **M. Varma, A. Zisserman, A Statistical Approach to Texture Classification from Single Images, Int. J. Comput. Vision 62, 1-2, 61-81, Apr. 2005. 11

12. A World to Be Measured 15 experimental datasets Many demonstrations – Arachnology – Forensic Science – Biology – Archeology – Biometrics – Historical Texts – Texture benchmarks – And more! A LOT of datasets! 12

13. One nearest neighbor, leave-one-out cross validation Texton measure is trained on the entire dataset All experiments, demonstrations, and figures are completely reproducible All datasets and source are available online Experiments and Reproducing! Hey Doc! Start reproducing at www.cs.ucr.edu/~bcampana/texture.html 13

14. Speed Check Because it’s simple! Go with blue!! Why is CK-1 so fast? 14

15. Perception is Key! Filter Bank CK-1 15

16. Performance at a Glance CK-1 is DEFINITELY a contender! 16

17. In Summary Presented a compression based framework and measure for texture. Simple. Empirically tested Freely and easily available. Fast. Accurate. 17 Simplicity, carried to an extreme, is elegance. -Jon Franklin

18. Contact Email bcampana@cs.ucr.edu Paper Support Site www.cs.ucr.edu/~bcampana/texture.html 18