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.
Highlights
With the development of electronic commerce, internet shopping for clothing has become a common lifestyle [1,2,3,4]
The experimental results demonstrate the superiority of enhanced capsule (EnCaps) network, which is attributed to the deeper network and optimal network structure
A novel EnCaps network is proposed for clothing image classification
Summary
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. It is difficult to distinguish the fine-grained classification of clothing by personal judgment. The high-efficiency method of the clothing classification [5,6] is urgent in the rapid development of clothing shopping
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