EnCaps: Clothing Image Classification Based on Enhanced Capsule Network

Abstract

Clothing image classification is more and more important in the development of online clothing shopping. The clothing category marking, clothing commodity retrieval, and similar clothing recommendations are the popular applications in current clothing shopping, which are based on the technology of accurate clothing image classification. Wide varieties and various styles of clothing lead to great difficulty for the accurate clothing image classification. The traditional neural network can not obtain the spatial structure information of clothing images, which leads to poor classification accuracy. In order to reach the high accuracy, the enhanced capsule (EnCaps) network is proposed with the image feature and spatial structure feature. First, the spatial structure extraction model is proposed to obtain the clothing structure feature based on the EnCaps network. Second, the enhanced feature extraction model is proposed to extract more robust clothing features based on deeper network structure and attention mechanism. Third, parameter optimization is used to reduce the computation in the proposed network based on inception mechanism. Experimental results indicate that the proposed EnCaps network achieves high performance in terms of classification accuracy and computational efficiency.

Application of Clothing Design Based on Computer Vision Technology Using Hybrid Graph Convolution Neural Network and Lotus Effect Optimization Algorithm

“…An extensive conversation was started by the company's productivity. Yu et al [26] have developed the online clothing shopping Current clothing shopping applications that was popular were recommendations for similar clothes, clothing commodity retrieval, and clothing category labeling. These applications rely on precise clothing picture classification technology.…”

An improved image processing algorithm for visual characteristics in graphic design

“…This article introduces CUP into the decoding part, composed of multiple up-sampling and multi-layer jump connections to decode the hidden features to get the final generated image ( Yu et al, 2021 ). After the sequence of hidden features is reconstructed , the up-sampling is realized by cascading multiple up-sampling blocks and skip connections.…”

“…In recent years, the convolutional neural network (CNN) has been widely used in image processing, which will cascade representation and classification learning. It can adaptively mine useful feature information from images through a backpropagation algorithm ( Yu et al, 2021 ). There are two kinds of CNN-based clothing image processing methods.…”

“… Ge et al (2019) constructed the DeepFashion data set and proposed the Match R-CNN network for clothing categories, key point detection, segmentation, and generation. Wang et al (2018) and Yu et al (2021) used dynamic dependence and symmetry to encode clothing images and introduced a bidirectional CNN network to model clothing images for feature extraction and key point detection. Lin et al (2016) extracted the pixels of the input image based on the depth confidence network and realized the classification and generation of clothing images.…”

Clothing image classification algorithm based on convolutional neural network and optimized regularized extreme learning machine

“…An increasing number of scholars have devoted themselves to the integration of clothing image classification and CNNs, and have achieved good results. Yu et al 17 proposed an enhanced capsule network with image features and spatial structure features to solve the problem in which a traditional neural network cannot obtain the spatial structure features of clothing images. The network model first acquired the spatial structural features of clothing images through an enhanced capsule network.…”