## 1 Introduction  
Texture pattern classification was considered an important problem in computer vision for many years because of the great variety of possible applications, including non-destructive inspection of abnormalities on wood, steel, ceramics, fruit, and aircraft surfaces [1; 2; 3; 4; 5; 6]. Texture discrimination remains a challenge since the texture of objects varies significantly according to the viewing angle, illumination conditions, scale change, and rotation [1; 4; 7; 8]. There is also the special problem of color image retrieval related to appearance-based object recognition, which is a major field of development for several industrial vision applications [1; 4; 7; 8]. Feature extraction of color, texture, and shape from images was used successfully to classify patterns by reducing the dimensionality and the computational complexity of the problem [3; 9; 10; 11; 12; 13; 14; 15; 16]. Determining the appropriate features for each problem is a recurring challenge which is yet to be fully met by the computer vision community [1; 3; 16]. Feature extraction and selection enable representation of the information present in the image, and limit the number of features, thus allowing further analysis within a reasonable time. Feature extraction was used in a wide range of applications, such as biometrics [12; 14; 15], classification of cloth, surfaces, landscapes, wood, and rock minerals [16; 17], saliency detection [18], and background subtraction [19], among others. During the past 40 years, while a substantial number of methods for grayscale texture classification were developed [3; 5], there was also a growing interest in colored textures [1; 2; 9; 10; 13; 20; 21]. The adaptive integration of color and texture attributes into the development of complex image descriptors is an area of intensive research in computer vision [21]. Most of these investigations focused on the integration process in applications for digital image segmentation [20; 22] or the aggregation of multiple preexisting image descriptors [23]. Deep learning was applied successfully to object or scene recognition [24] and scene classification [25], and the use of deep neural networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations was investigated [26]. Current approaches to color texture analysis can be classified into three groups: the parallel approach, the sequential approach, and the integrative approach [11; 27]. The parallel approach considers texture and color as separate phenomena. Color analysis is based on the color distribution in an image, without regard to the spatial relationship between the intensities of the pixels. Texture analysis is based on the relative variation of the intensity of the neighbors, regardless of the color of the pixels. In Reference [2], the authors first converted the original RGB images into other color spaces: HSI (Hue, Saturation, Lightness), CIE XYZ (Comission Internationale de l'Eclairage Tristimus values), YIQ (Luminance In phase Quadrature), and CIELAB (Comission Internationale de l'Eclairage Lightness-Green-Blue), and then extracted the texture features and color separately. In a similar manner, as reported in Reference [13], the images were transformed to the color spaces HSV and YCbCr, obtaining wavelet intensity channel features of first-order statistics on each channel. The choice of the best performing color space was an open question in recent years, since using one space instead of another can bring considerable improvements in certain applications [28]. The quaternion representation of color was shown to be effective in segmentation in Reference [29] and the feature extraction method, binary quaternion moment preserving (BQMP), is a method of image binarization using quaternions, with the potential for being a powerful tool in color image analysis [6]. In the sequential approach to color texture analysis, the first step is to apply a method of indexing color images.