1. Texture Detection & Texture related clustering C601 Project Jing Qin Fall 2003

2. Outline  Introduction  PCA based texture representation  Texture detection  Texture related image clustering  Future works

3. Introduction  What is “textures”? Webster’s: Webster’s: Something composed of closely interwoven elementsSomething composed of closely interwoven elements The structure formed by the threads of a fabricThe structure formed by the threads of a fabric The visual or tactile surface characteristics and appearance of somethingThe visual or tactile surface characteristics and appearance of something Etyma: L textura, fr. Textus, (to weave)Etyma: L textura, fr. Textus, (to weave) Others: grain, pattern of wood, water,granite Others: grain, pattern of wood, water,granite

4. Introduction (Cont.)  CS Definition: Formalized terms: Formalized terms: Basic elementsBasic elements Pixels Pixels Small patterns Small patterns Relations (repetition) of elementsRelations (repetition) of elements statistics statistics grammar grammar Descriptive Def: Descriptive Def: Those similar enough to a set of textures samples would be of the same texturesThose similar enough to a set of textures samples would be of the same textures What do we mean by saying: “similar”,then?What do we mean by saying: “similar”,then?

5. Introduction (Cont)  Statistical Texture Description spatial frequencies spatial frequencies Edge frequencies Edge frequencies Primitive length Primitive length ……. …….  Syntactic texture description Shape chain grammars Shape chain grammars Graph grammars Graph grammars

6. Texture Representation  PCA (Principal Component Analysis): Project the samples (points) perpendicularly onto the axis of ellipsoid Project the samples (points) perpendicularly onto the axis of ellipsoid Rotates the ellipsoid to be parallel to the coordinate axes Rotates the ellipsoid to be parallel to the coordinate axes Use the fewer and more important coordinates to represent the original samples Use the fewer and more important coordinates to represent the original samples  Transforms of PCA: The first a few eigenvectors of covariance matrix

7. Texture Representation (Cont.)  How to represent textures using PCA? Select primary textures (6,7) we need to consider (manually) Select primary textures (6,7) we need to consider (manually) Use texture samples (16×16 texture images) as points in PCA Use texture samples (16×16 texture images) as points in PCA Compute the eigenvector (PCA transform) using those 6 or 7 256 dimensional vectors with PCA. Compute the eigenvector (PCA transform) using those 6 or 7 256 dimensional vectors with PCA. Use the Eigen-textures generated through PCA transform as the texture representation Use the Eigen-textures generated through PCA transform as the texture representation

8. -2.9768 2.5950 0.0013 0.0324 -0.0120 -0.0000 7 primary textures (16*16 blocks) manually selected to compute through PCA 6- dimensional Eigen textures generated for the texture No.1 (256-dim converted to 6- dim)

9. Texture detection  Compare the image to the texture representation (similarity match)  Texture detection based on PCA PCA Transform PCA Transform Compare Eigen-images to Eigen-textures Compare Eigen-images to Eigen-textures Euclidean distanceEuclidean distance Texture Segmentation Texture Segmentation

10. Texture Detection (1st Ver) Dividing the target image into (overlapping) blocks with the same size as the 7 primary textures, use the PCA transform and compare them to the eigen-textures (compute the euclidean distance) Only use texture detection, 6 clusters generated

11. Revised Version  Revised version Intuition: reduce the influence of light condition Intuition: reduce the influence of light condition Calibrate (Generalize) grey level with the texture sample before using PCA Calibrate (Generalize) grey level with the texture sample before using PCA Check the grey level differenceCheck the grey level difference Reduce/increase the grey level of the image blocks accordinglyReduce/increase the grey level of the image blocks accordingly Better? Better? Problems? Problems?

12. Texture related image clustering  Color clustering Method used k-mean k-mean  Use texture information as fourth dimension (colors as the other three)  Add certain weight to the fourth dimension (200 or 300, why?)  Evaluation of textures information The more two textures are similar to each other, the closer their ‘texture’ value should be The more two textures are similar to each other, the closer their ‘texture’ value should be

13. Results of Original K-mean PCA-clustering results (4 dimension, without formalizing grey level)

14. Final version of clustering First use (grey level) formalized PCA texture detection, then cluster using k-mean, based on texture information combined with 3 color dimensions,

15. Future Works  Texture is not only repeated elements Reflectivity & refractivity Reflectivity & refractivity Combination of other texture principles Combination of other texture principles  Samples size Large Large Small Small  Samples selection  Problems with PCA: scalability

16. Reference  Image Processing, Analysis, and Machine Vision Milan Sonka, Vaclav Hlavac, and Roger Boyle 1998 Image Processing, Analysis, and Machine Vision Milan Sonka, Vaclav Hlavac, and Roger Boyle Image Processing, Analysis, and Machine Vision Milan Sonka, Vaclav Hlavac, and Roger Boyle  http://www.cs.berkeley.edu/~daf/bookpage s/slides.html http://www.cs.berkeley.edu/~daf/bookpage s/slides.html http://www.cs.berkeley.edu/~daf/bookpage s/slides.html  Merriam-Webster’s Collegiate Dictionary, Tenth Edition