# EnCaps: Clothing Image Classification Based on Enhanced Capsule Network
## 4 Discussion and Future Directions  
To realize the more accurate classification of clothing images, we propose the EnCaps network, which uses spatial structure extraction, enhanced feature extraction, and parameter optimization to obtain spatial structure information and robust image feature of clothing images. The EnCaps network not only achieves high accuracy of classification, but also low parameter computation. The experimental results demonstrate the superiority of EnCaps network, which is attributed to the deeper network and optimal network structure. The accuracy and computational complexity are the key metrics that we should consider in the network design. The traditional classification network does not consider the spatial structure feature, and the feature may improve the classification accuracy based on the previous works. Thus, the concept of capsule network is fused into the designed network. The original capsule network is not suitable for clothing classification, and the proposed network is designed anew according to the demand of clothing classification. The input of image size is enlarged by modifying the input network, and more robust feature extraction is obtained by the deeper and more efficient Encaps network. Comparison with traditional capsule network, the network structure is designed anew, and the classification accuracy and efficiency are remarkably improved. The classical deep learning network focuses on the image feature but spatial location relationship. The improvement of network depth and structure is usually considered to extract robust objective features, such as LeNet [34], AlexNet [35], VGGNet [29], GoogleNet [30], ResNet [31], DenseNet [32], MobileNet [33], YOLO [36; 37; 38; 39], and so on. The improvement may be valid for the classical objection classification, such as pedestrian, vehicle, animal, and other objectives with obvious image features. Clothing classification belongs to the fine-grained classification which has inconspicuous features, and the classification methods based on image feature without spatial location feature for difficulty achieving impressive results. Maybe the EnCaps network and other similar networks, which can extract spatial location relationships, are the best choice for the fine-grained classification task. From the experimental results in the clothing classification, two phenomena are worth discussing. First, the EnCaps network achieves the best performance in terms  
Figure 11: The effectiveness of the EnCaps network in detecting garment classification. of top-1 accuracy among VGGNet, GoogleNet, ResNet, DenseNet, MobileNet, and the traditional Capsule network. Generally, the more complex and deeper network may obtain better performance, but EnCaps, which has the lowest computational cost besides MobileNet, which is a lightweight network for mobile devices, obtains the best accuracy. The phenomenon may demonstrate that the spatial location information plays an important part in the procedure of classification. Second, the EnCaps network obtains the best accuracy among all compared methods with a gradually increasing training set from beginning to end. The larger training set is used, and the more accurate performance will be achieved in general. In our experiment, the recognition model is trained by 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000 samples, respectively. The EnCaps network obtains the best performance without exception, which may demonstrate that the spatial location information can boost the efficiency of training models with a small set of samples. The development of artificial intelligent technology will change the nature of the clothing industry. In the future, more and more intelligent applications will be used for clothing. A system may identify your current clothing and recommend your favorite clothing. Clothing image classification is the fundamental technology in more complex applications, such as evaluation of clothing compatibility, clothing recommendation, and fashion trend prediction.