## 3 Results
### Results  
#### 3.2.1 Brodatz Database  
Table 1 shows the classification results for the Brodatz database. In this experiment, we used the features extracted in the first set (10 images), varying the size of the images in training \(\mathrm{r}\times\mathrm{n}^{2}\). The last column shows the results of using the post-processing stage applied on the column with the best performance. The best result for the first experiment on the Brodatz database using 10 images was 100%. In this case, 27 (\(3\times 3^{2}\)) images with size \(71\times 71\) (213/3) pixels were used for training. It can also be observed that the use of the post-processing step improved the results up to 100% in all cases. These results were higher than 98.23%, the best result previously published for this experiment on the Brodatz database [1]. Also, Table 1 shows the classification results for the second, third, and fourth experiments on the Brodatz database with 30, 40, and 111 images, respectively. The best result reached using 30 images was 99.84%. In this case, 64 (\(4\times 4^{2}\)) random images with size \(53\times 53\) (213/4) pixels were used for training. It can also be observed that the use of the post-processing step improved  
the results up to 100% in all cases. These results are higher than the best result, 97.54%, previously published for this experiment on the Brodatz database [1]. The best result reached using 40 images was 99.71%. In this case, 100 (\(4\times 5^{2}\)) random images of size 42 \(\times\) 42 (213/5) pixels were used for training. It can also be observed that the use of the post-processing step improved the results up to 100% in all cases. These results are higher than 95.5%, the best previously published result for this experiment on the Brodatz database [1]. The best result reached using all the 111 images was 97.63%. In this case, 196 (\(4\times 7^{2}\)) random images of size 30 \(\times\) 30 (213/7) pixels were used for training. It can also be observed that the use of the post-processing step improved the results up to 99.88%. These results are higher than the bests result of 98.25% and 99.5% previously published for this experiment on the Brodatz database [30; 50]. The size of the smaller images reached an optimum for n = 7 with an image size of 30 \(\times\) 30. We performed an exhaustive search varying from n = 2 to n = 9, reaching an optimum at n = 7. An explanation is that n = 7 is optimal for the complete method using texture and color features. Table 2 compares the results previously published in the literature and our results on the Brodatz database for the four experiments which included 10, 30, 40, and 111 images, respectively. It can be seen in Table 2 that our method, with post-processing, reached the highest results. The most significant improvement was reached on the complete Brodatz database that includes 111 images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Rest Results vs. Number of Images**} & **10** & **30** & **40** & **111** \\ \hline [PERSON] et al., 2005 [43] & - & - & - & 88.15\% \\ [PERSON] et al., 2008 [44] & - & - & - & 89.71\% \\ [PERSON] et al., 2011 [1] & 98.23\% & 97.54\% & 95.5\% & 91.03\% \\ [PERSON] el al, 2017 [50] & - & - & - & 98.25\% \\ [PERSON] et al., 2017 [31] & - & - & - & 97.84\% \\ \hline SVM global–local Haralick BOMP & 100\% & 99.84\% & 99.71\% & 97.63\% \\ method 1  & \\ \hline KNN 2  & global–local Haralick BOMP & 100\% & 99.94\% & 99.98\% & 94.46\% \\ method + post-processing 1  & SVM global–local Haralick BOMP & 100\% & 100\% & 100\% & 99.88\% \\ SVM global–local Haralick BOMP & 100\% & 100\% & 100\% & 100\% & 99.88\% \\ method + post-processing 1  & & & & 99.77\% \\ method + post-processing 1  & & & & 99.77\% \\ method + post-processing 1  & & & & 99.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Best results of global–local Haralick– binary quaternion-moment-preserving (BQMP) classification for the Brodatz database for the four sets of 10, 30, 40, and 111 images compared to previously published studies.