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Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement

Research and development

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Nr DOI: 10.5604/12303666.1215535

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Abstract:

The purpose of this paper was to develop an intelligent recognition system consisting of a feature reduction method combining cluster and correlation analyses, and a probabilistic neural network (PNN) classifier to identify different types of hip shape from 3D measurement for each person. Firstly 28 items reflecting lower body part information of 300 female university students aging from 20 to 24 years were selected. The feature reduction method was employed to extract typical indices. Secondly hip shapes were subdivided into five types by a K-means cluster and analysis of variance (ANOVA). Finally the PNN was then trained to serve as a classifier for identifying five different hip shape types. The average classification accuracy of the scheme proposed was 97.37%, and its effectiveness was successfully validated by comparing with the BP and Support Vector Machine (SVM) scheme. Thus an intelligent recognition system was developed to make hip shape type classification of high-precision and time saving.

Tags:

intelligent recognition system, probabilistic neural network, classification accuracy, feature reduction, typical index, cluster analysis.

Citation:

Jin J, Yang Y, Zou F. Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement. FIBRES & TEXTILES in Eastern Europe 2016; 24, 5(119): 110-118. DOI: 10.5604/12303666.1215535

Published in issue no 5 (119) / 2016, pages 110–118.

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FIBRES & TEXTILES in Eastern Europe 19/27 M. Skłodowskiej-Curie Str., 90-570 Łódź, Poland e-mail: infor@lit.lukasiewicz.gov.pl; ftee@lit.lukasiewicz.gov.pl

EDITORIAL DEPARTMENT
Editor-in-Chief Dariusz Wawro, Head of Editorial Office Janusz Kazimierczak, Text Editor Geoffrey Large, Assistant Editor Anna WahlProduction Łukasiewicz-ŁIT

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