Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images
The average accuracy of the fusion of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.77% higher than that of NP-DCNN. Traditional image aesthetic...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2021/8619449 |
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| author | Feifeng Liu Weihu Wang |
| author_facet | Feifeng Liu Weihu Wang |
| author_sort | Feifeng Liu |
| collection | DOAJ |
| description | The average accuracy of the fusion of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.77% higher than that of NP-DCNN. Traditional image aesthetic evaluation methods are only effective for specific image sets or specific style images and are not suitable for all types of images. Based on the introduction of the partial differential equation image filtering method, through the parallel supervised learning of aesthetic attribute labels, this paper extracts the global aesthetic depth features, adopts the partial differential equation to evolve the contour C constant, and constructs a convolution neural network. The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. Through the aesthetic evaluation experiment, the overall test accuracy is improved by 0.58% and the average accuracy of the integration of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.83% higher than that of NP-DCNN. It has achieved better test accuracy than before in the seven subcategories with discrimination between high aesthetic and low aesthetic images. It has achieved better classification performance than the traditional feature extraction methods in the public dataset CUHK database, and it has excellent aesthetic reference value. |
| format | Article |
| id | doaj-art-01bbd5ee27b442f491740e898ed117e6 |
| institution | OA Journals |
| issn | 1687-9139 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-01bbd5ee27b442f491740e898ed117e62025-08-20T02:01:49ZengWileyAdvances in Mathematical Physics1687-91392021-01-01202110.1155/2021/8619449Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of ImagesFeifeng Liu0Weihu Wang1School of ArtsThe School of Computer and Information ScienceThe average accuracy of the fusion of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.77% higher than that of NP-DCNN. Traditional image aesthetic evaluation methods are only effective for specific image sets or specific style images and are not suitable for all types of images. Based on the introduction of the partial differential equation image filtering method, through the parallel supervised learning of aesthetic attribute labels, this paper extracts the global aesthetic depth features, adopts the partial differential equation to evolve the contour C constant, and constructs a convolution neural network. The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. Through the aesthetic evaluation experiment, the overall test accuracy is improved by 0.58% and the average accuracy of the integration of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.83% higher than that of NP-DCNN. It has achieved better test accuracy than before in the seven subcategories with discrimination between high aesthetic and low aesthetic images. It has achieved better classification performance than the traditional feature extraction methods in the public dataset CUHK database, and it has excellent aesthetic reference value.http://dx.doi.org/10.1155/2021/8619449 |
| spellingShingle | Feifeng Liu Weihu Wang Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images Advances in Mathematical Physics |
| title | Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images |
| title_full | Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images |
| title_fullStr | Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images |
| title_full_unstemmed | Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images |
| title_short | Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images |
| title_sort | application of partial differential equation image classification methods to the aesthetic evaluation of images |
| url | http://dx.doi.org/10.1155/2021/8619449 |
| work_keys_str_mv | AT feifengliu applicationofpartialdifferentialequationimageclassificationmethodstotheaestheticevaluationofimages AT weihuwang applicationofpartialdifferentialequationimageclassificationmethodstotheaestheticevaluationofimages |