1. A Statistical Approach to Texture Classification Nicholas Chan Heather Dunlop 16-720 Project Dec. 14, 2005

2. Introduction Purpose: classify materials from their imaged appearance without any knowledge of illumination or viewing conditions Use statistical method by Varma and Zisserman Similar to assignment 2

3. Introduction Columbia-Utrecht Reflectance and Texture (CUReT) database

4. Introduction A single texture can appear vastly different with changes in illumination and viewing direction eg. Pebbles

5. The Algorithm Texton library generation from Varma and Zisserman, 2005

6. The Algorithm Model generation from Varma and Zisserman, 2005

7. The Algorithm Classification from Varma and Zisserman, 2005

8. Filters Rotationally invariant filters: MR8 One Gaussian: σ x = σ y = 10 One Laplacian of Gaussian: σ x = σ y = 10

9. Filters Edge filterBar filter At 3 scales: (σ x, σ y ) = {(1,3), (2,6), (4,12)} And 6 orientations Take maximum filter response over all orientations

10. Filters Rotationally invariant Isotropic and anisotropic filters Only 8 dimensions Leung-Malik filter set:Schmid filter set:

11. Pre-processing Input images: Convert to monochrome Crop to central 128x128 region Normalize: zero mean and unit standard deviation Filters: Normalize: unit L 1 norm Filter response: Normalize:

12. Textons by Clustering 5 images are chosen for each texture The filter responses are aggregated K-means is used to create 10 clusters The cluster centers are the textons The 50 textons are collected into a library

13. Textons by Clustering Example textons:

14. Model Generation The texton distribution of each training image is computed and used as a model Each texture class is represented by a set of histograms Example histograms:

15. Classification An image is classified by computing its histogram and choosing the closest model from the histogram set Distance metric is χ 2 statistic: H = computed image histogram h = model histogram

16. Experiments A training set is used for texton library and model generation Classification accuracy assessed on the test set Using assignment 2 textures: Texton and model generation: 5 textures, 5 images per texture Testing: all given testing images Using CUReT database: Texton generation: 5 images for each of 10 textures Model generation: 15 images per texture Testing: 14 images per texture

17. Results Classification accuracy Assignment 2: 86.3% averaged over 5 trials CUReT database:

18. Extensions 3 scales for Gaussian and Laplacian of Gaussian Because these features may also appear at multiple scales

19. Extensions Take max filter response over orientations and response at orthogonal direction Because some textures have features at more than one orientation Textons from Leung-Malik filter set (not rotationally invariant): from Varma and Zisserman, 2005

20. Results Averaged over 3 trials Original Multiple Scales Orthogonal Orientation Both Assignment 286.3%87.4%80.6%82.3% CUReT Database

21. Analysis The unique characteristics of this algorithm are good: Rotational invariance Clustering in low dimensional space Other orientations beyond the maximum response one are useful The accuracy is better than assignment 2 using Gabor filters

22. Conclusions Classify single images using only a few models of each texture Do not require knowledge of imaging conditions (illumination and viewing direction) Rotationally invariant, low dimensional, maximum response filter banks

23. References Varma, M. and Zisserman, A. “A statistical approach to texture classification from single images.” International Journal of Computer Vision: Special Issue on Texture Analysis and Synthesis, to appear in 2005. Varma, M. and Zisserman, A. “Classifying Images of Materials: Achieving Viewpoint and Illumination Independence.” Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark (2002).