# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
###### Abstract  
Convolutional Neural Networks (CNNs) have recently been proposed as a solution in texture and material classification in computer vision. However, inside CNNs, the internal layers of pooling often cause a loss of information and, therefore, is detrimental to learning the architecture. Moreover, when considering images with repetitive and essential patterns, the loss of this information affects the performance of subsequent stages, such as feature extraction and analysis. In this paper, to solve this problem, we propose a classification system with a new pooling method called Discrete Wavelet Transform Pooling (DWTIP). This method is based on the image decomposition into sub-bands, in which the first level sub-band is considered as its output. The objective is to obtain approximation and detail information. As a result, this information can be concatenated in different combinations. In addition, wavelet pooling uses wavelets to reduce the size of the feature map. Combining these methods provides acceptable classification performance for three databases (CIFAR-10, DTD, and FMD). We argue that this helps eliminate overfitting and that the learning graphs reflect that the datasets show learning generalization. Therefore, our results indicate that our method based on wavelet analysis is feasible for texture and material classification. Moreover, in some cases, it outperforms traditional methods. 2022  
2022  
analysis--a methodology that can provide a better classification performance of textures and materials [6]. In this sense, a fusion of methods in order to address this problem is being used, combining spatial and spectral approaches in a unique architecture [7; 8]. Despite the results obtained by the authors in texture classification, their architecture only merges the features that are lost with the spatial approach. In addition, it is known that regularization methods focus only on the convolutional layer. While the operations of the pooling layers have been left without an update [9]. In this sense, we can integrate wavelet analysis inside deep learning before merging spatial and spectral approaches; that is, we permit it to become part of the learning process by using the pooling method. Motivated by the above reasons, in this study, we propose a classification system with a new pooling method called Discrete Wavelet Transform Pooling (DWTP). The pooling approach is based on image decomposition into sub-bands. The method is implemented and developed using Python and Keras API with Tensorflow as Backend. Moreover, the method is validated on three datasets: CIFAR-10, Describable Textures Dataset (DTD), and Flickr Material Database (FMD). Our approach is different from traditional methods because it is not a subsampling methodology by using neighboring regions, but wavelet pooling maintains its function as a reduction layer. Wavelets allow localization in scale (i.e., frequency) and space. In other words, wavelets can be used to analyze local and spatial transients in the data, such as edges or surfaces in an image [10]. Therefore, we can preserve the most relevant information of textures and materials, which is sometimes lost with traditional methods such as Max-Pooling (MaxP) and Ave-Pooling (AveP). In a previous study, as support for this work in [11], we designed a CNN architecture for object detection with a repetitive pattern approach within aerial navigation as a first attempt. We argue that the characteristics at different frequencies, low and high, also affect the performance of the CNN during training. This architecture is characterized by wavelet analysis, applying multiresolution analysis to the original image. A new dataset is obtained when the image is converted to the wavelet domain. Therefore, the information improves learning performance, eliminates overfitting, and achieves higher efficiency in object detection.