Clothing recognition based on deep sparse convolutional neural network
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 25 February 2022
Issue publication date: 1 March 2022
Abstract
Purpose
The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.
Design/methodology/approach
The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.
Findings
Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.
Originality/value
Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.
Keywords
Acknowledgements
Funding: The research was funded by National Natural Foundation of China (No: 61976105) and National Key R&D Program of China (No: 2017YFB0309200).
Citation
Xiang, J., Pan, R. and Gao, W. (2022), "Clothing recognition based on deep sparse convolutional neural network", International Journal of Clothing Science and Technology, Vol. 34 No. 1, pp. 119-133. https://doi.org/10.1108/IJCST-06-2018-0081
Publisher
:Emerald Publishing Limited
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