Texture Detection & Texture related clustering

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: • Something composed of closely interwoven elements • The structure formed by the threads of a fabric • The visual or tactile surface characteristics and appearance of something • Etyma: L textura, fr. Textus, (to weave) • Others: grain, pattern of wood, water,granite

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

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

6. Texture Representation • PCA (Principal Component Analysis): • Project the samples (points) perpendicularly onto the axis of ellipsoid • Rotates the ellipsoid to be parallel to the coordinate axes • 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) • 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. • Use the Eigen-textures generated through PCA transform as the texture representation

8. 7 primary textures (16*16 blocks) manually selected to compute through PCA -2.9768 2.5950 0.0013 0.0324 -0.0120 -0.0000 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 • Compare Eigen-images to Eigen-textures • Euclidean distance • 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 • Calibrate (Generalize) grey level with the texture sample before using PCA • Check the grey level difference • Reduce/increase the grey level of the image blocks accordingly • Better? • Problems?

12. Texture related image clustering • Color clustering Method used • 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

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

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 • Combination of other texture principles • Samples size • Large • Small • Samples selection • Problems with PCA: scalability

16. Reference • Image Processing, Analysis, and Machine VisionMilan Sonka, Vaclav Hlavac, and Roger Boyle1998 • http://www.cs.berkeley.edu/~daf/bookpages/slides.html • Merriam-Webster’s Collegiate Dictionary, Tenth Edition