An artistic analysis model based on sequence cartoon images for scratch
With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created...
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| Published in: | International journal of intelligent systems Vol. 37; no. 11; pp. 9598 - 9619 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
John Wiley & Sons, Inc
01.11.2022
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| ISSN: | 0884-8173, 1098-111X |
| Online Access: | Get full text |
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| Abstract | With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created on it are displayed in the form of cartoon clips. It is valuable to explore the visual aesthetics embodied in these clips to fill the gap in the assessment system. We propose a model that predicts the human view scores of cartoon clips created on Scratch. Our method is divided into two steps to evaluate the aesthetic of the sequence images that compose cartoon clips. First, we train an image classification network to predict the relative aesthetics of individual images. Then we construct an aesthetic space for the sequence image and improve the rating within a specific range. We put forward ScratchGAN to generate a Scratch‐cartoon‐style aesthetic analysis data set for training the classification network. Experimental results show that our Generative Adversarial Network framework can well transform photos into a Scratch‐cartoon style. The single image assessment network can generate predictions that fit human cartoon aesthetic opinions. Our method achieves satisfactory results in the aesthetic evaluation of sequence cartoon images. |
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| AbstractList | With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created on it are displayed in the form of cartoon clips. It is valuable to explore the visual aesthetics embodied in these clips to fill the gap in the assessment system. We propose a model that predicts the human view scores of cartoon clips created on Scratch. Our method is divided into two steps to evaluate the aesthetic of the sequence images that compose cartoon clips. First, we train an image classification network to predict the relative aesthetics of individual images. Then we construct an aesthetic space for the sequence image and improve the rating within a specific range. We put forward ScratchGAN to generate a Scratch‐cartoon‐style aesthetic analysis data set for training the classification network. Experimental results show that our Generative Adversarial Network framework can well transform photos into a Scratch‐cartoon style. The single image assessment network can generate predictions that fit human cartoon aesthetic opinions. Our method achieves satisfactory results in the aesthetic evaluation of sequence cartoon images. |
| Author | Chai, Xiaolin Luo, Hong Guizani, Mohsen Sun, Yan |
| Author_xml | – sequence: 1 givenname: Xiaolin orcidid: 0000-0002-7447-3367 surname: Chai fullname: Chai, Xiaolin organization: Beijing University of Posts and Telecommunications – sequence: 2 givenname: Yan orcidid: 0000-0002-8192-9545 surname: Sun fullname: Sun, Yan email: sunyan@bupt.edu.cn organization: Beijing University of Posts and Telecommunications – sequence: 3 givenname: Hong surname: Luo fullname: Luo, Hong organization: Beijing University of Posts and Telecommunications – sequence: 4 givenname: Mohsen surname: Guizani fullname: Guizani, Mohsen organization: University of Idaho |
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| Cites_doi | 10.1109/CVPR.2012.6247954 10.1109/CVPR.2018.00986 10.1109/WACV.2018.00226 10.1145/3240508.3240554 10.1109/ICIP.2008.4711702 10.1109/IWCMC.2018.8450296 10.1109/WACV.2017.58 10.1109/TMM.2015.2477040 10.1109/CVPR.2017.84 10.1109/CVPR42600.2020.00811 10.1109/TPAMI.2015.2389824 10.1145/2493394.2493403 10.1109/TIP.2018.2831899 10.1609/aaai.v33i01.3301679 10.1109/ICCV.2015.119 10.1109/CVPR.2016.60 10.1145/2647868.2654927 10.1109/ICASSP.2018.8462533 10.1145/1592761.1592779 10.1109/TMM.2013.2269899 10.1109/CVPR.2019.00960 10.1007/978-981-15-5577-0_18 10.1007/978-3-319-46448-0_40 10.1109/MSP.2017.2696576 10.1007/978-3-319-24261-3_7 |
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| SubjectTerms | Aesthetics Clips deep learning Evaluation generative adversarial network Generative adversarial networks image aesthetic Image classification images classification Intelligent systems scratch sequence cartoon images visual programming Visual programming languages |
| Title | An artistic analysis model based on sequence cartoon images for scratch |
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