## 4 Discussion  
The idea of combining color and texture was proposed previously, but the proposed feature extraction process allows the method to preserve the information available in the original image, yielding significantly better results than those previously published. A possible drawback of previous texture classification methods is that important information is lost from the original image with the feature extraction method, hampering its ability to improve texture classification results. The feature extraction process that includes global and local features is something new from the point of view of combining color with texture. Sub-dividing the training partition of the database into sub-images, and trying to obtain all the information present in the image using various image sizes or a different number of images is something that was not reported in previous publications.  
Color and texture features are extracted in order to classify complex colored textures. However, the feature extraction process loses part of the information present in the image because the two-dimensional (2D) information is transformed into a reduced space. By using global and local features extracted from many different partitions of the image, the information needed for colored texture classification is preserved better. Sub-dividing the training data into sub-images (local-global) and trying to obtain all the information present using different image sizes is a new approach.  
Although the BQMP method was proposed several years ago [6], it was used in color data compression, color edge detection, and multiclass clustering of color data. The reduction of an image into representative colors and a histogram that indicates which part of the image is represented by these colors achieves excellent results. In addition, local and global features are extracted from each image. The results of our method were compared with those of several other feature extraction implementations on the Brodatz database with those published in References [1; 27; 28; 31; 43; 44; 50], on the Vistex database with those published in References [4; 9; 13; 23; 27; 28; 30; 38; 45; 46; 50], on the Outex database with those published in References [4; 8; 9; 11; 27; 28; 30; 38; 45; 46; 50], and on the KTH-TIPS2b with those published in References [7; 8; 31; 34; 35] (please see Tables 2, 4, 6 and 8). The proposed method generated better results than those that were published previously.  
The databases Brodatz, Vistex, Outex, and KTH2b-Tips are available for comparing the results of different texture classification methods. Tests should be performed under the same conditions. We compared our results with those of References [1; 7] under the same conditions using the same training/test distribution, and an SVM as a classifier. We also compared our results with those of Reference [4] in which they used a nearest-neighbor classifier (KNN) and, therefore, we tested our method with KNN instead of SVM. The results with KNN are shown in Tables 2, 4, 6 and 8, corroborating that SVM achieves better results. The proposed method achieved better results than those previously published.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vistex** & **FE Time (s)** & **SVM Time (s)** & **PP Time (s)** & **Total Time (s)** \\ \hline Training & 57.05 s & 305.19 s & 44.96 s & 102.86 s \\ Test & 55.88 s & 41.22 s & 5.76 s & 407.2 s \\ Total & 112.93 s & 346.41 s & 50.72 s & 510.06 s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computational time of the proposed method on feature extraction (FE), SVM classification time, postprocessing (PP) time and total time. Experiments were conducted on the Vistex database (54 images).