Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (05): 146-152.doi: 10.13475/j.fzxb.20190802707

• Apparel Engineering • Previous Articles     Next Articles

Classification and recognition of body type for young boys based on improved fast search and finding of density peak algorithms

ZHOU Jie(), MAO Qian   

  1. Apparel and Art Design College, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2019-08-09 Revised:2020-02-12 Online:2020-05-15 Published:2020-06-02

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Abstract:

In view of the notable changes in body type caused by the obesity among young boys and in order to optimize the body type recognition of young boys, the study was carried out on school age boys of 7 to 10 years old. To start with, the weights of 8 body type features were calculated through grey correlation. Then, the study used weighted clustering by fast search and finding of density peaks (CFSFDP)algorithm to classify and analyze the body types of boys. Finally, the research established the model of body type recognition for boys with the extreme learning machine. The results show that the weighted CFSFDP algorithm has a strong interpretation for the classification of body type in boys. The 8 body type features of young boys approximately form an arithmetic sequence with the tolerance with age; the 7-year-old boys has the smallest body type difference while the body types of 10-year-old boys show diversity. The accuracy of body type recognition for CFSFDP clustering is 70%, and that for weighted CFSFDP clustering increase to 90%. The improved CFSFDP algorithm therefore is recognized as effective to improve the accuracy and robustness of body type recognition for young boys.

Key words: children's wear design, body type classification for young boys, body type recognition for young boys, feature weight of body type, clustering algorithm

CLC Number: 

  • TS941.17

Fig.1

ELM model of body type recognition"

Fig.2

Research process"

Tab.1

Eight feature weights for body type"

人体特征 权重 人体特征 权重
F1 0.142 9 F5 0.110 7
F2 0.142 7 F6 0.114 1
F3 0.141 7 F7 0.116 2
F4 0.115 9 F8 0.115 8

Fig.3

Decision diagram of weighted CFSFDP cluster"

Tab.2

Features of four class cluster centerscm"

类簇中心 F1 F2 F3 F4 F5 F6 F7 F8
1 125 58 33 55 46 66 75 50
2 135 61 35 55 49 69 80 54
3 140 65 36 61 49 73 84 56
4 145 68 37 64 52 77 90 59

Fig.4

Two-dimensional distribution of sample data"

Tab.3

Difference of features about cluster centerscm"

类簇差值
统计量		 F1 F2 F3 F4 F5 F6 F7 F8
3-2差值 5.0 4.0 1.0 6.0 0.0 4.0 4.0 2.0
4-3差值 5.0 3.0 1.0 3.0 3.0 4.0 6.0 3.0
均值 5.0 3.5 1.0 4.5 1.5 4.0 5.0 2.5
标准差 0.0 0.5 0.0 1.5 1.5 0.0 1.0 0.5

Tab.4

Mean of features of four class clusterscm"

类簇中心 F1 F2 F3 F4 F5 F6 F7 F8
1 126 59 32 56 46 66 75 51
2 133 64 33 59 48 73 80 54
3 140 68 35 63 50 74 83 55
4 149 73 37 69 52 81 89 60

Fig.5

Age distribution of cluster samples"

Fig.6

Accuracy of type recognition. (a) Clustering by CFSFDP; (b) Weighted clustering by CFSFDP"

Fig.7

Influence of number of hidden layer neurons on ELM performance"

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