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Erschienen in: Cluster Computing 2/2019

05.01.2018

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

verfasst von: Sun Feifei, Xu Pinghua, Ding Xuemei

Erschienen in: Cluster Computing | Sonderheft 2/2019

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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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Metadaten
Titel
Multi-core SVM optimized visual word package model for garment style classification
verfasst von
Sun Feifei
Xu Pinghua
Ding Xuemei
Publikationsdatum
05.01.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1651-4

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