Clothing image classification algorithm based on convolutional neural network and optimized regularized extreme learning machine

This paper proposes a new method that uses Alexnet with ImageNet transfer learning as the feature extractor and optimized and regularized extreme learning as the classifier. We keep the first five convolutional layers and the first two fully connected layers of Alexnet, and then train the network. T...

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Vydáno v:Textile research journal Ročník 92; číslo 23-24; s. 5106 - 5124
Hlavní autoři: Zhou, Zhiyu, Liu, Mingxuan, Deng, Wenxiong, Wang, Yaming, Zhu, Zefei
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.12.2022
Sage Publications Ltd
Témata:
Algorithms
Artificial neural networks
Classification
data collection
fabrics
Feature extraction
image analysis
Image classification
Learning algorithms
Machine learning
Neural networks
Optimization
Regularization
Runge-Kutta method
Transfer learning
Runge Kutta optimization algorithm
Clothing image classification
convolutional neural network
regularized extreme learning machine
ISSN:0040-5175, 1746-7748, 1746-7748
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Shrnutí:This paper proposes a new method that uses Alexnet with ImageNet transfer learning as the feature extractor and optimized and regularized extreme learning as the classifier. We keep the first five convolutional layers and the first two fully connected layers of Alexnet, and then train the network. Then, the mutual information between each dimension of the feature and its category is calculated and sorted, and the feature with the highest ranking is selected for feature dimensionality reduction. The regularization penalty term is introduced to the extreme learning machine to control its algorithm complexity and solve the problem of overfitting. Finally, the Runge Kutta optimization algorithm is employed to ameliorate the hidden layer bias and input weight of the regularized extreme learning machine, and the optimized regularized extreme learning machine is used to classify the dimensionality-reduced clothing image traits. The test outcome illustrates that on some apparel classification with style (ACWS) datasets, the precision, recall, F1-score, and accuracy of the proposed algorithm are 93.06%, 93.17%, 92.82%, and 93.14%, respectively, which are better than those of other clothing image classification algorithms. The results verify that the raised algorithm significantly ameliorates the classification property of clothing image algorithms.
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ISSN:0040-5175
1746-7748
1746-7748
DOI:10.1177/00405175221115472