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. 845-858 Speaker: Tzu-Chuen Lu

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

Image Classification ?

Image Segmentation

Image features

Image Classification

Association Rules

Association Rules

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

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

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

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

Association rules for image data

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

Association rules for image data Min confidence = 1

Texture 1 Texture 2 Texture 3 Texture 4

Texture 1 Texture 2 Texture 3 Texture 4

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

Association rules for texture classification

Association rules for texture classification

Image Segmentation

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

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