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• W4289201746 abstract "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." @default.
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• W4289201746 date "2022-08-01" @default.
• W4289201746 modified "2023-10-17" @default.
• W4289201746 title "Clothing image classification algorithm based on convolutional neural network and optimized regularized extreme learning machine" @default.
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• W4289201746 doi "https://doi.org/10.1177/00405175221115472" @default.
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