LBP More Slides from Dr Shahera Hossain Pixel

LBP & More Slides from Dr. Shahera Hossain

Pixel Neighborhood-based Feature • The most important for texture analysis is to describe the spatial behavior of intensity values in any given neighborhood. • Different methodologies have been proposed. • Local binary pattern (LBP) is one of the most-widely used approach – mainly for face recognition. • LBP is used for texture analysis too.

Local Binary Pattern (LBP) For each PIXEL of an image, a BINARY CODE is produced to make a new matrix with the new value (binary to decimal value).

Computation of Local Binary Pattern Binary code for > Nc 3 7 2 8 4 1 2 3 5 0 1 Nc 0 0 0 1 Component-wise multiplication 0 2 0 128 ? 0 0 0 16 ∑ Sum LBP 146 Representation Neighborhood of a gray-scale image 1 2 4 128 8 64 32 16 Example of how the LBP operator works top

Computation of LBP (LSB) Binary Pattern: 1 (MSB) 1 0 1 1 1 x 2 7 1 x 2 6 1 x 2 5 1 x 2 4 0 x 2 3 = 128 LBP: = 64 = 32 = 16 0 0 1 (LSB) 0 x 2 2 0 x 2 1 1 x 2 0 = 0 = 1 0 = 0 1 + 0 + 0 + 16 + 32 + 64 + 128 = 241

A New Method: DCLBP • A new method called diagonal-crisscross local binary pattern (DCLBP) for texture representation is proposed recently. • Basic concept: An image feature should take diagonal pixel variations as well as horizontal and vertical (crisscross) pixel variations in the neighborhood, so that it can perform well even in the cases of rotations in images. • Two other variants of the LBP, considering rotational feature & mean/median of the neighbor pixels are also proposed • These are compared with LBP, median-LBP, interpolation. LBP, number-LBP, neighborhood-intensity-LBP.

Diagonal-Crisscross Local Binary Pattern (DCLBP) (a) N 1 – N 5 N 0 – N 4 (b) N 2 – N 6 (LSB) N 3 – N 7 (c) (d) top

Diagonal-Crisscross Local Binary Pattern (DCLBP)… a) 3 x 3 image patch b) Start from N 0 pixel position c) Get differences for front-diagonal values (N 0 – N 4), in vertical direction (N 1 – N 5), for back-diagonal direction (N 2 – N 6), and in horizontal direction (N 3 – N 7) 1 1 3 5 7 d) Multiply each difference with 2 , 2 and 2 sequentially to 35 7 compute a new value e) Take the mean value of newly-computed value and the central pixel value

Diagonal-Crisscross Local Binary Pattern… • p 0 = 21 p 1 = 23 p 2 = 25 p 3 = 27

Diagonal-Crisscross Local Binary Pattern… •

Median-RILBP •

Mean-RILBP •

Experiment: Dataset 1 Figure: Sample images for each class of USC-SIPI Rotated Textures dataset. Figure: Seven rotated images for bark class (USC-SIPI Rotated Textures dataset).

Experiment: Dataset 1 – USC-SIPI DB • USC-SIPI (i. e. , University of Southern California – Signal and Image Processing Institute)) Rotated Textures dataset • Each image is digitized at seven different rotation angles: 0, 30, 60, 90, 120, 150, and 200 degree for each of the 13 different images • These images are taken from the Brodatz database which is the most widelyused dataset USC SIPI Database Total classes 13 No. of images per class 7 Rotations 7 Image size 512 x 512 pixels Total images 91

Experiment: Dataset 2 Ten different classes for KTH TIPS dataset (sandpaper, crumpled aluminum foil, styrofoam, sponge, corduroy, linen, cotton, brown bread, orange peel and cracker B)

Experiment: Dataset 2 – KTH-TIPS Database • KTH-TIPS (TIPS stands for ‘Textures under varying Illumination, Pose and Scale’) database. KTH TIPS Database Total classes 10 No. of images per class 81 Rotations & variations 81 Image size 200 x 200 pixels Total images 810

Classification • Lets employ – K-nearest neighbor classifier (KNN) & – Multi-class support vector machine (SVM) (radial basis kernel function, gamma = 1). We consider 10 -fold cross-validation

Recognition Results – 1 USC SIPI Database Total classes 13 No. of images per class 7 Rotations 7 Image size 512 x 512 Total images 91 USC SIPI Rotated Textures DB Feature KNN SVM LBP 46. 0 74. 7 Interpolation_LBP 46. 1 80. 2 Median_LBP 30. 8 80. 2 Median. R_LBP 84. 6 93. 4 Number_LBP 92. 3 87. 9 NI_LBP 23. 1 65. 9 Mean-RILBP 100 95. 6 Median-RILBP 92. 3 96. 7 DCLBP 100 90. 1

Recognition Results – 2 KTH TIPS Database Total classes 10 No. of images per class 81 Rotations & variations 81 Image size 200 x 200 Total images 810 Feature LBP KTH TIPS DB KNN 80 Interpolation_LBP Median. R_LBP Number_LBP NI_LBP Mean-RILBP Median-RILBP DCLBP 60 70 70 90 70 70 70 90 SVM 53. 7 53. 1 46. 4 28 35. 2 55. 7 23. 4 29. 6 72. 5 top

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