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Multi-core SVM optimized visual word package model for garment style classification

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Abstract

One clothing style classification research algorithm based on multi-core support vector machine (SVM) optimized visual word package model has been proposed to improve the computational performance of clothing style classification algorithm effectively. Firstly, detection technique of human body parts has been adopted to locate key parts of clothing and remove redundant information, which improves the accuracy of attribute classification; secondly, multi-core SVM characteristics has been applied to map input clothing style from original data space to high-dimensional data space. Lagrange method has been adopted to realize dual solving of original problem and then realize clothing style multi-core SVM classification recognition based on Mercer principle; finally, effectiveness of the proposed method has been verified through experimental test.

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Acknowledgements

This research is supported by the Fundamental Research Funds for the Central Universities (GrantNo.CSQ13038), Culture research funds for Hubei Ethnic Affairs Commission (Grant No. QSZ13009) and Nantong Science and Technology Project (Grant No.GY12016032).

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Authors and Affiliations

  1. Fashion and Art Design Institute, Donghua University, Shanghai, 200051, China

    Sun Feifei & Ding Xuemei

  2. Fine Arts Academy, South-Central University For Nationalities, Wuhan, 430073, Hubei, China

    Sun Feifei

  3. School of Fashion Design & Engineering, Zhejiang SCI-TECH University, Hangzhou, 310018, Zhejiang, China

    Xu Pinghua

  4. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai, 200051, China

    Xu Pinghua & Ding Xuemei

Authors
  1. Sun Feifei
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  2. Xu Pinghua
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  3. Ding Xuemei
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Correspondence to Sun Feifei.

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Feifei, S., Pinghua, X. & Xuemei, D. Multi-core SVM optimized visual word package model for garment style classification. Cluster Comput 22 (Suppl 2), 4141–4147 (2019). https://doi.org/10.1007/s10586-017-1651-4

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  • DOI: https://doi.org/10.1007/s10586-017-1651-4

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