## 1 Introduction
As a result, indexed images are processed obtaining grayscale textures. The co-occurrence matrix was used extensively since it represents the probability of occurrence of the same color pixel pair at a given distance [9]. Another example of this approach is based on texture descriptors using three different color indexing methods and three different texture features [11]. This results in nine independent classifiers that are combined through various schemes. Integration models are based on the interdependence of texture and color. These models can be divided into single bands, if the data of each channel is considered separately, or a multiband, if two or more channels are considered together. The advantage of the single-band approach is the easy adaptation of classical models based on a grayscale domain, such as Gabor filters [15; 30; 31; 32], local binary patterns (LBP) or variants [5; 7; 8; 33; 34; 35; 36; 37; 38], Galois fields [39], or Haralick statistics [3]. In Reference [2], the main objective was to determine the contribution of color information for the overall performance of classification using Gabor filters and co-occurrence measures, yielding results almost 10% better than those obtained with only grayscale images. In Reference [4], the results reported using co-occurrence matrices reached 94.41% and 99.07% on the Outex and Vistex databases, respectively. Different classifiers, such as k-nearest neighbors, neural networks, and support vector machines (SVM) [40], are used to combine features. The latter was proven to be more efficient when feature selection is performed [41; 42] or clustering is used [22]. There are other methods that reached the best results in color texture analysis on databases that are publicly available. In Reference [1], a multi-scale architecture (Multi-Scale Supervised self-OrganizingOrientational-invariant Neural or multi-scale SOON) was presented that reached 91.03% accuracy on the Brodatz database, which contains 111 different colored textures. These results were compared with those of two previous studies on the same database, reported in References [43; 44], which reached classification rates of 89.71% and 88.15%, respectively. Another approach used all the information from sensors that created the image [4]. This method improved the results to 98.61% and 99.07%, on the same database, but required a non-trivial change in the architecture of the data collection. A texture descriptor based on a local pattern encoding scheme using local maximum sum and difference histograms was proposed in Reference [8]. The experimental results were tested on the Outex and KTH-TIPS2b databases reaching 95.8% and 91.3%, respectively. In References [32; 33; 36; 39], the methods were not tested in the complete databases. In References [26; 45; 46], the methods used a different metric to calculate the classification. A new method for the classification of complex colored textures is proposed in this paper. The images are divided into global and local samples where features are extracted to provide a new representation into global and local features. The feature extraction from different samples of the image using quadrants is described. The extraction of the features in each of the image quadrants, obtaining color and texture features in different spatial scales, is presented: the global scales using the whole image, and the local scales using quadrants. This representation seems to capture most of the information present in the image to improve colored texture classification. Then, a support vector machine classification that was performed is reported and, finally, the post-processing stage using the bagging that was implemented is presented. The proposed method was tested on four different databases: Colored Brodatz Texture [20], VisTex [4], Outex [47], and KTH-TIPS2b [48] databases. The subdivision of the training partition of the database into sub-images while extracting information from each sub-image of different sizes is also new.