# EnCaps: Clothing Image Classification Based on Enhanced Capsule Network
## 1 Introduction  
With the development of electronic commerce, internet shopping for clothing has become a common lifestyle [1, 2, 3, 4]. Before the clothing information is uploaded to the online shopping mall, the category, texture, style, fabric, and shape of clothing should be labeled. The purchaser searches for suitable clothing by keyword retrieval. The manual label method may be very costly on a human level, and the correct labeling of clothing is based on personal judgment. The mistake of personal judgment is inevitable in the thousands of clothing updates. Furthermore, it is difficult to distinguish the fine-grained classification of clothing by personal judgment. Thus, the high-efficiency method of the clothing classification [5, 6] is urgent in the rapid development of clothing shopping. Clothing classification attracts a lot of attention in academic circles. The classification methods for clothing are usually divided into two categories. First, the traditional feature extraction methods for clothing classification can be also divided into two types, one is based on global shape features and global texture features [7], such as Fourier descriptors, geometric invariant distance, local binary patterns (LBP), etc., and another one is based on local feature methods that include scale-invariant feature transformation (SIFT), speed up robust features (SURF), histogram of oriented gradient (HOG), etc. [8, 9, 10, 11]. The classification accuracy of traditional methods rely on the selective feature severely. In specific cases, the methods may achieve high-level accuracy with stable and conspicuous features. Generally, the clothing image is various, similar, and complex. The traditionalmethods can not extract robust features for classification. Second, with the technological development of convolutional neural network (CNN), CNN is widely used in clothing classification [12; 13; 14]. The method achieves better performance than the traditional methods. It extracts clothing features based on the deep network without manual setting, which can obtain robust clothing features [15; 16]. The convolution network and polling operations are used to extract the clothing feature, and deeper networks may achieve better performance in general. However, the CNN only extracts the image features without a spatial relationship between different local features [17; 18; 19; 20]. The traditional CNN can not breakthrough the bottleneck of classification accuracy. Capsule network [21; 22; 23; 24] is a novel CNN which is proposed to obtain the spatial relationship of different features, which extract the spatial relationship by the dynamic routing algorithm. The fundamental feature unit is expressed by the capsule, which is a vector. However, the traditional capsule network [21] is usually used in the MNIST database for handwritten digits recognition, and the size of input is defined as 28 \(\times\) 28. With the advantage of the capsule network in spatial features, Ref. [25] proposes a novel multi-scale capsule network to extract the multi-scale feature. Ref. [26] proposes a subspace capsule network to exploit the idea of capsule networks to model possible variations in the appearance or implicitly-defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules. Ref. [27] proposes a multi-lane capsule networks that is a separable and resource efficient organization of capsule networks that allows parallel processing while achieving high accuracy at reduced cost. In a word, the traditional capsule network and improved capsule networks can not efficiently extract robust clothing feature, which are not suitable for the accurate classification of clothing images. In order to further improve the accuracy of clothing image classification, we propose an enhanced feature capsule (EnCaps) network. The traditional convolutional neural network can not obtain the spatial location relationship.