# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 2 Related Works  
Recently, there has been increased interest in the e-commerce fashion industry. Researchers have addressed several clothes-related problems such as clothing classification [6; 7; 8; 9], attribute recognition [10; 11; 12], and fashion recommendation systems [13]. Some works have addressed specific attribute recognition such as fiber identification [14; 15]. Others classify clothes images according to their pattern (texture) attribute [16; 17; 18; 19].  
As per image or texture recognition techniques, clothing pattern recognition methods are categorized in two main groups. First are traditional methods that adopt handcrafted feature extraction methods to extract useful features and then classify them using some classifiers such as Support Vector Machine (SVM), neural networks (NNs), or other classifiers. Moreover, texture feature extraction methods can either extract local features, such as Scale-Invariant Feature Transformation (SIFT), Speeded Up Robust Features (SURFs), and Histogram of Oriented Gradient (HOG), or global features, such as Radon Signature, Discrete Wavelet Transform (DWT), Gray-Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP). [PERSON] et al. [16] proposed systems that used SIFT, Radon Signature, and DWT to extract local and global features. However, in [PERSON]'s [17] work, SURF was used to capture local features and the global features were captured using DWT and GLCM, while both systems fed the extracted features into the SVM classifier. [PERSON] [18] also utilized GLCM to extract global features but fed them to a random forest classifier. The second approach is the deep learning approach, which has been applied successfully in texture recognition in general and for clothes specifically [14; 19]. [PERSON] et al. [19] classified clothes images into one of six common patterns: solid, striped, checkered, dotted, zigzag, and floral. They adopted the state-of-the-art convolutional neural network model (ResNet-101 [5]) that was pre-trained on an ImageNet dataset [20]. Using a standard transfer learning approach, they fixed all layers except for the final densely connected classification layer and trained the weights for that layer using their dataset. [PERSON] et al. [21] proposed a modified convolutional neural network (MCNN) that employs a random tuning strategy to optimize hyperparameters, specifically tailored to traditional Indonesian woven fabrics. It demonstrates superior performance compared to several pre-trained CNN models. [PERSON] et al. [22] proposed a network that employs LSTM to handle the complexities of sequential patterns in fabric textures, resulting in improved fabric texture classification and defect detection. The proposed method in [23] used a video-based fabric pattern recognition approach with a Bayesian-optimized CNN (Bayes Opt-CNN). Video streams of fabric surfaces are used to extract spatiotemporal features, while Bayesian optimization selects the best hyperparameters to improve the accuracy of pattern recognition. The work in [24] proposed a dual-branch network that incorporates local features extracted from a pre-trained ResNet 50 model with global features extracted by Residual Pooling Transformer (RPT) for general texture recognition.  
Most of the recent works that utilize deep learning approach identify clothes patterns as one of several attributes resulting in poor pattern recognition accuracy. However, most of the works carried out from the perspective of clothes patterns specifically relied on handcrafted features combined with a machine learning classifier.  
This work propose a clothes pattern recognizer that can be part of an assistive tool for visually impaired people. It is an ensemble classifier that constitutes three sub-models: a pre-trained ResNet50 model combined with a non-local block (NL), a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block where the voting is based on entropy.