Mesh Segmentation Based on Optimizing Extreme Learning Machine with Ant Lion Optimization

Xiaowen Yang; Honghong Yin; Xie Han; Jiaming Liu

doi:10.3788/LOP57.041014

Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041014(2020)

Mesh Segmentation Based on Optimizing Extreme Learning Machine with Ant Lion Optimization

Xiaowen Yang^*, Honghong Yin, Xie Han, and Jiaming Liu

Author Affiliations

School of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China

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Figures & Tables(7)

Fig. 1. Overall flow chart of our method

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Fig. 2. Convergence of Airplane model at different population sizes

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Fig. 3. Convergence of Octopus model at different population sizes

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Fig. 4. Segmentation results of six types of models

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Fig. 5. Bar chart showing average segmentation accuracy obtained with the methods in references[6-8] and our method

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Table 1. Comparison of average segmentation accuracy obtained with the methods in references [6-8] and our method

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Table 1. Comparison of average segmentation accuracy obtained with the methods in references [6-8] and our method

Dataset	Average segmentation accuracy /%
Dataset	CGF[2014]	ATG[2015]	CEA[2017]	Ours
Airplane	94.49	97.74	96.20	97.00
Ant	95.98	98.84	97.59	98.07
Chair	97.41	98.53	97.73	98.05
Octopus	98.73	98.87	-	99.49
Teddy	97.74	98.53	98.44	98.79
Fish	95.68	96.74	96.35	97.35

Table 2. Time consumption of our method during training the model with 200000-300000 patches
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Table 2. Time consumption of our method during training the model with 200000-300000 patches
Dataset Total number of patches Training time / s
Airplane 253954 997.49
Ant 299156 1112.56
Fish 239886 901.69

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Xiaowen Yang, Honghong Yin, Xie Han, Jiaming Liu. Mesh Segmentation Based on Optimizing Extreme Learning Machine with Ant Lion Optimization[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041014

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Paper Information

Category: Image Processing

Received: Jul. 11, 2019

Accepted: Aug. 12, 2019

Published Online: Feb. 20, 2020

The Author Email: Yang Xiaowen (Wenyang1314@nuc.edu.cn)

DOI:10.3788/LOP57.041014

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of average segmentation accuracy obtained with the methods in references [6-8] and our method

Table 1. Comparison of average segmentation accuracy obtained with the methods in references [6-8] and our method

Table 2. Time consumption of our method during training the model with 200000-300000 patches

Table 2. Time consumption of our method during training the model with 200000-300000 patches