# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 1 Introduction  
In recent years, the increased number of e-commerce platforms and online fashion retailers such as clothing recommendation systems, inventory management, and fashion trend analysis tackle vast amounts of image data in the textile and clothing field. This requires robust solutions for recognizing and retrieving clothes with specific properties, using machine learning algorithms to extract semantic information from images instead of relying on manual annotations. From another perspective, by virtue of the information explosion in this era, raw data captured by mobile devices and fed into powerful computation do not only facilitate daily life but can also increase the independence and confidentiality of disabled persons. Choosing clothes with appropriate designs and patterns is one of the challenging tasks for visually impaired people. Tackling this issue with an automatic assistive technique instead of family help would be preferred. Generally, automated clothing pattern recognition can significantly enhance efficiency in industrial applications and improve personalized fashion experiences. Generally, as stated in [1], the texture of an image is identified by the variance in the spatial distribution of pixel intensities. As a result, texture classification is the task of identifying and recognizing this pattern distribution of pixels. To some extent, the texture classification task is similar to the object recognition task in that the strong correlation of pixel intensities is determined. However, the pixel arrangement in a small portion ofan image is repeated within the whole image, and thus the pattern of the image can be recognized even with small portions. On the other hand, in object recognition, the entire object should appear for accurate recognition. Determining the pattern of clothes is a texture classification task where repeated basic primitives, such as dots, stripes, plaids, floral, etc., characterize the pattern. Figure 1 shows some popular patterns in our clothes. In the context of the computer vision field, extracting the pattern of clothing from natural photos is a challenging process due to variations in lighting, angles, and zoom if taken as selfies or from advertising boards on city street compared to professional photos. Moreover, images of clothing patterns contain a high degree of intraclass variations for the same pattern category compared to traditional texture images such as grass, wood, leaves, etc., shown in popular texture datasets such as Brodatz [2] that express less intraclass and intensity variation. Traditional texture descriptors in the literature extract useful information related to image texture and are represented as a feature vector before classification. Local Binary Pattern (LBP) [3], Gray-Level Co-occurrence Matrices (GLCMs) [4], and Gabor filter banks are some of the most relevant texture descriptors that are widely used, especially with insufficiency in resources. Recently, deep learning methods, usually using convolutional neural networks (CNNs), have outperformed traditional techniques. CNNs are composed of convolutional, pooling, and fully connected layers and learn hierarchical features using convolutional layers at different levels; lower layers extract low-level features, and higher-level layers learn semantics. They have become popular due to their remarkably good performance in a wide range of visual recognition tasks, such as object recognition, compared to handcrafted features. This work proposes a solution based on CNN models for the problem of clothes patterns. Usually, there is a small-scale dataset for clothes patterns and limited computational resources, which are not enough to train a deep CNN model and avoid the problem of overfitting. To overcome these issues, we propose an ensemble classifier that uses a pre-trained ResNet50 with non-local block (NL), a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block as base learners. The predictions of base learners are fused using a novel fusion technique based on entropy voting.