Research on She nationality clothing recognition based on color feature fusion with PSO-SVM
Author:
Ding Xiaojun1, Li Tao1, Chen Jingyu12, Zou Fengyuan132
Affiliation:
1. School of Fashion Design and Engineering, Zhejiang Sci-Tech University , Hangzhou , Zhejiang 310018 , China 2. Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Zhejiang Sci-Tech University , Hangzhou , Zhejiang 310018 , China 3. Engineering Research Center of Clothing of Zhejiang Province, Zhejiang Sci-Tech University , Hangzhou , Zhejiang 310018 , China
Abstract
Abstract
Although the color characteristics of She nationality clothing are slightly different, there are multiple similarities in shapes and textures. Therefore, it is difficult to effectively distinguish different branches of She nationality clothing. To address this problem, this article, taking into account color feature fusion, proposes a recognition method based on a hybrid algorithm of particle swarm optimization and support vector machine (PSO-SVM). First, the color histogram and color moment (CM) feature descriptors were extracted from the five branches of She nationality clothing, and the color feature distribution of each branch was obtained. Then, color feature fusion is performed through optimization and dimensionality reduction of principal components. Furthermore, PSO was introduced to independently optimize parameter combinations. Finally, the different branches of She nationality clothing were automatically recognized. The results demonstrated that the proposed method could effectively distinguish different branches of She nationality clothing. Compared with the recognition accuracy of approaches using single-color histogram and CM feature, the performance of our proposed method was increased by 5.25 and 6.44%, respectively. When the penalty parameter
γ
\gamma
and kernel parameter
δ
2
{\delta }^{2}
of SVM were 123.29 and 1.16, respectively, the recognition accuracy of the model was the highest, reaching 98.67%. The proposed method could be a reference for the subdivision recognition of She nationality clothing.
Publisher
Walter de Gruyter GmbH
Subject
General Materials Science
Reference18 articles.
1. Chen, L. Y. (2012). Evolution causes analysis of She’s ancient costume from perspective of cultural change. Journal of Textile Research, 33(17), 111–115. 2. Nawaz, M. M. T., Hasan, R., Hasan, M. A., Hassan, M., Rahman, R. M. (2018). Automatic categorization of traditional clothing using convolutional neural network. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 98–103). IEEE, Singapore. 3. Fu, B. L., Liu, X. G. (2019). An intelligent computational framework for the definition and identification of the womenswear silhouettes. International Journal of Clothing Science and Technology, 31(2), 158–180. 4. Wu, H., Ding, X. J., Li, Q. M., Du, L., Zou, F. Y. (2019). Classification of women’s trousers silhouette using convolution neural network CaffeNet model. Journal of Textile Research, 40(4), 117–121. 5. Ding, X., Zou, C., Chen, J., Zou, F. (2016). Extraction and classification of She nationality clothing via visual features. Textile Research Journal, 86(12), 1259–1269.
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