## 3 Results
### Results
#### 3.2.5 Color or Texture vs. Color and Texture  
Table 9 compares the results of color features, texture features, and of the combination of both, for texture classification measured on the Brodatz, Vistex, Outex, and KTH-TIPS2b databases. It can be observed that both types of features, color and texture, contribute to the overall results, with maximum performance when both types of features are combined. Comparing these results to those previously published on the same databases, it can be observed that, although the method reached 100% on the Vistex database in Reference [28], the results yielded on Outex were only 94.5%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**n \({}^{2}\) Random Images \({}^{1}\) in Training**} \\ \hline \multicolumn{5}{c}{**Without Post-Processing**} & \multicolumn{2}{c}{**With Post-Processing**} \\ \hline \multirow{3}{*}{S = 1} & n = 2 & n = 1 & n = 2 \\  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \cline{1-1}  & n = 1 & n = 2 & n = 1 & n = 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification results of the experiment on the KTH-TIPS2b database for the four sets of 1188 images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Method** & **Result** \\ \hline [PERSON] et al., 2015 [7] & Divisive information theoretic clustering (DITC) & 70.6\% \\ [PERSON] et al., 2016 [8] & 3D-ASDH & 91.3\% \\ [PERSON] et al., 2018 [34] & Local concave/convex micro-structure pattern & 84.44\% \\ [PERSON] et al., 2019 [35] & Attractive-and-Repulsive & 93.61\% \\  & Center-Symmetric -LBP & 92.90\% \\ \hline \multicolumn{3}{c}{Global–local Haralick BOMP method \({}^{1}\)} & 92.90\% \\ KNN global–local Haralick BOMP method + post-processing \({}^{1}\) & 92.72\% \\ Global–local Haralick BOMP method + post-processing \({}^{1}\) & 95.75\% \\ \hline \hline \end{tabular} \({}^{1}\) The last three methods are in this paper.  
\end{table}
Table 8: Best results of global–local Haralick–BOMP classification for the KTH-TIPS2b database with 1188 images per set and the best previously published results on the same database.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & \begin{tabular}{c} **Global–Local Haralick** \\ **BOMP Method with All** \\ **Features** \\ \end{tabular} & \
\begin{tabular}{c} **Global–Local Haralick** \\ **BOMP Method with** \\ **Only Color Features** \\ \end{tabular} &
\begin{tabular}{c} **Global–Local Haralick** \\ **BOMP Method with** \\ **Only Texture Features** \\ \end{tabular} \\ \hline Brodatz & 99.88\% & 96.28\% & 87.84\% \\ Outex & 98.97\% & 91.86\% & 88.57\% \\ Vistex & 100\% & 95.67\% & 90.84\% \\ KTH-TIPS2b & 95.75\% & 94.95\% & 92.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Best results of global–local Haralick–BOMP classification for the all databases with the contribution of each part of the model (color and spatial structure).