Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/inderscience-publishers/research-on-the-extraction-method-of-painting-style-features-based-on-abSxDOkrMG
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- Inderscience Publishers
- Copyright
- Copyright © Inderscience Enterprises Ltd
- ISSN
- 1754-8853
- eISSN
- 1754-8861
- DOI
- 10.1504/ijart.2022.122448
- Publisher site
- See Article on Publisher Site
Abstract
In order to overcome the problem of low accuracy in the traditional method for style feature extraction of painting works, this paper proposes a method for style feature extraction of painting works based on convolution neural network. Firstly, the parameters in the digital image of painting works are quantised, and then the feature parameters are fused by fusion technology and used as input information. Then the wind fusion features of painting works are extracted by using the deep hash coding of triple recombination structure in convolutional neural network. The experimental results show that the precision value of this method always stays at a high level with the change of the recall value, which can be kept above 0.7, and the AP value is always above 0.9. It shows that this method has strong adaptability and high precision of feature extraction.
Journal
International Journal of Arts and Technology – Inderscience Publishers
Published: Jan 1, 2022