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- W4200529690 abstract "In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities." @default.
- W4200529690 created "2021-12-31" @default.
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- W4200529690 date "2021-12-29" @default.
- W4200529690 modified "2023-09-28" @default.
- W4200529690 title "Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm" @default.
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- W4200529690 doi "https://doi.org/10.1155/2021/5349916" @default.
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