1. Using Association Rules as Texture features
Authors: J.A. Rushing, H.S. Ranganath,
T.H. Hinke, and S.J. Graves
Source: IEEE Transactions on Pattern
Analysis and Machine Intelligence,
Vol. 23, No. 8, pp
Speaker: Tzu-Chuen Lu

2. Outline Introduction Image classification and segmentation
Association rules for image data
Association rules for texture classification
Experimentations
Conclusions

3. Image Classification ?

4. Image Segmentation

5. Image features

6. Image Classification

7. Association Rules

8. Association Rules

9. Association rules for image data
X = 0
X = 1
X = 2
X = 3
X = 4
Y = 0
Y = 1
Y = 2
Y = 3
Y = 4
N*N image

10. Association rules for image data
3*3 pixels
Root pixel 1 2 3 4
N*N image

11. Association rules for image data
1-Item: (X, Y, I)

12. Association rules for image data
{(0, 0, 0), (1, 0, 2)}
{(0, 0, 2), (1, 1, 2)}

13. Association rules for image data

14. Association rules for image data
Sup ({(0, 0, 0)}) = 3, Sup ({(1, 0, 2)}) = 4

15. Association rules for image data
Min confidence = 1

17. Texture 1
Texture 2
Texture 3
Texture 4

18. Texture 1
Texture 2
Texture 3
Texture 4

19. Suite 1: man made textures
Suite 2: natural textures
Suite 3 : suite 1 + suite 2
Train samples: 32
Test samples: 32

20. Association rules for texture classification

21. Association rules for texture classification

22. Image Segmentation

23. (a) Association Rules (b) Gabor filter (c) GLCM
Image Segmentation
(a) Association Rules (b) Gabor filter (c) GLCM

24. Conclusions New texture features based on association rules
Classification and segmentation
Time complexity