A. Fernández, M.X. Álvarez and F. Bianconi

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Abstract
Code
Data

Results

Datasets

Number

Name

Zip file

Classes

Samples
per class

Total
samples

1
Bonn BTF
BonnBTF.zip
10
16
160
2
Brodatz
Brodatz.zip
13
16
208
3
Jerry Wu
JerryWu.zip
39
4
156
4
KTH-TIPS
KTH-TIPS
10
4
40
5
KTH-TIPS2b
KTH-TIPS2b
11
16
176
6
MondialMarmi
MondialMarmi.zip
12
64
768
7
Outex_TC_00000
OUTEX_TC_00000.zip
24
20
480
8
Outex_TC_00001 OUTEX_TC_00001
24
88
2112
9
Outex_TC_00013 OUTEX_TC_00013
68
20
1360
10
UIUCTex
UIUCTex.zip
25
40
1000
11
VisTex
VisTex.zip
167
16
2672

When uncompressed, each zip file yields a folder which contains:

  • classes.txt: one row per class with the following fields arranged by columns: numeric index and text ID.

  • dataset.txt: one row per sample with the following fields arranged by columns: numeric index, file name and class label.

  • problems.txt: names of the different subdivisions of the samples intro train and test sets.

  • images: subfolder which contains the texture image samples.

  • sets: subfolder which contains as many couples of train_xxx.txt and test_xxx.txt files as there are problems (100 in this setting), where xxx holds for the name of a particular problem. Each file stores the numeric indices of the samples that make up the corresponding set.

In each problem half of the samples of each class is used for training and the other half for testing and accuracy estimation. The following example explains how the procedure works:

Images
Train
numbers
Test
numbers
Train
images
Test
images
Class_A_1.bmp
1

Class_A_1.bmp
Class_A_2.bmp

2

Class_A_2.bmp
Class_A_3.bmp

3

Class_A_3.bmp
Class_A_4.bmp
4

Class_A_4.bmp
Class_B_1.bmp
5

Class_B_1.bmp
Class_B_2.bmp 6

Class_B_2.bmp
Class_B_3.bmp
7

Class_B_3.bmp
Class_B_4.bmp
8

Class_B_4.bmp
Class_C_1.bmp 9

Class_C_1.bmp
Class_C_2.bmp
10

Class_C_2.bmp
Class_C_3.bmp 11

Class_C_3.bmp
Class_C_4.bmp
12

Class_C_4.bmp
Class_D_1.bmp 13

Class_D_1.bmp
Class_D_2.bmp
14

Class_D_2.bmp
Class_D_3.bmp
15

Class_D_3.bmp
Class_D_4.bmp 16

Class_D_4.bmp


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