# Multiscale Analysis for Improving Texture Classification
## 3 Proposed Approach  
This section describes how the proposed method integrates a multiresolution analysis and multiple texture descriptors for texture classification. To this end, we put forward an architecture structured in five main stages. Figure 2 shows an overview of the proposed scheme, and Algorithm 1 shows the steps of the first three stages. Figure 1: An example of Gaussian pyramids from the same input image. (**b**) First three levels of the Gaussian pyramid. Multiresolution representation: In this stage, for an input image, we generate three others corresponding to the levels of the Gaussian pyramid (L0: original image, L1: first level of the pyramid, L2: second level of the pyramid). The purpose is to represent an input image in different resolutions to capture intrinsic details. Channel splitting: Each channel (R, G, B) is considered a separate input in this phase. The key reason behind the splitting of the channels is to exploit color information. Thus, we represent and characterize an input image in a given resolution by a set of local descriptors generated from the interaction of a pixel with its neighborhood inside a given channel (R, G, or B). Feature extraction: After the channel-splitting step, the images undergo feature extraction, which looks for intrinsic properties and discriminative characteristics. For each GP level of an image, we extract BiT [9] (14-dimensional), Shannon entropy and multi-information, i.e., the total information (3-dimensional), Haralick [4] (13-dimensional), and GLCM [4] (6-dimensional) descriptors from each channel. Images are then represented by the concatenation of different measurements organized as feature vectors (Algorithm 1). For simplicity, we name the resulting descriptor _three-in-one_ (TiO). It is worth mentioning that images are converted to a gray scale for GLCM and Haralick measurements to be extracted but remain in color for extracting the bioinspired indices. After feature extraction and before concatenation, the feature vectors have dimensions of 126, 117, 54, and 18 for the BiT, Haralick, GLCM, and Shannon entropy and multi-information descriptors, respectively. Thereby, after concatenation, we have a 315-dimensional feature vector. Normalization: Because test points simulate real-world data, we split the data into training and test sets and perform a min-max normalization on the training data. Subsequently, we use the same min-max values to normalize the test set. Training/classification: We have a normalized feature vector comprising the concatenation of features extracted from each resolution and color channel from the previous stage. This texture representation is used to train one monolithic and three ensemble-based models. The linear discriminant analysis (LDA) is used for the monolithic classification model, while ensemble models are created using the histogram-based algorithm for building gradient boosting ensembles of decision trees (HistoB), the light gradient boosting decision trees (LightB), and the CatBoost classifier ([[https://catboost.ai/](https://catboost.ai/)]([https://catboost.ai/](https://catboost.ai/)), accessed on 12 January 2023), which is an efficient implementation of the gradient boosting algorithm. Figure 2: An overview of the proposed scheme. It is worth mentioning that the method proposed in this work to improve texture classification concentrates on a multiscale analysis of textural descriptors computed from the surface of 2D images. Step 3.1 of the algorithm splits the image into different channels, to wit, R, G, and B. The rationale is that there is a difference between processing a 3-channel RGB image and splitting the R, G, and B planes separately in texture analysis. Processing a 3-channel RGB image involves analyzing the image as a single entity, usually a single grayscale image composed of proportions of the R, G, and B channels, i.e., without separating the R, G, and B planes. This is valuable when the patterns or features contained in the image are correlated across multiple planes.