Quality-guided video aesthetics assessment with social media context
Media aesthetic assessment is a key technique in computer vision, which is widely applied in computer game rendering, video/image classification. Low-level and high-level features fusion-based video aesthetic assessment algorithms have achieved impressive performance, which outperform photo- and mot...
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| Published in | Journal of visual communication and image representation Vol. 71; p. 102643 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1047-3203 1095-9076 |
| DOI | 10.1016/j.jvcir.2019.102643 |
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| Abstract | Media aesthetic assessment is a key technique in computer vision, which is widely applied in computer game rendering, video/image classification. Low-level and high-level features fusion-based video aesthetic assessment algorithms have achieved impressive performance, which outperform photo- and motion-based algorithms, however, these methods only focus on aesthetic features of single-frame while ignore the inherent relationship between adjacent frames. Therefore, we propose a novel video aesthetic assessment framework, where structural cues among frames are well encoded. Our method consists of two components: aesthetic features extraction and structure correlation construction. More specifically, we incorporate both low-level and high-level visual features to construct aesthetic features, where salient regions are extracted for content understanding. Subsequently, we develop a structure correlation-based algorithm to evaluate the relationship among adjacent frames, where frames with similar structure property should have a strong correlation coefficient. Afterwards, a kernel multi-SVM is trained for video classification and high aesthetic video selection. Comprehensive experiments demonstrate the effectiveness of our method. |
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| AbstractList | Media aesthetic assessment is a key technique in computer vision, which is widely applied in computer game rendering, video/image classification. Low-level and high-level features fusion-based video aesthetic assessment algorithms have achieved impressive performance, which outperform photo- and motion-based algorithms, however, these methods only focus on aesthetic features of single-frame while ignore the inherent relationship between adjacent frames. Therefore, we propose a novel video aesthetic assessment framework, where structural cues among frames are well encoded. Our method consists of two components: aesthetic features extraction and structure correlation construction. More specifically, we incorporate both low-level and high-level visual features to construct aesthetic features, where salient regions are extracted for content understanding. Subsequently, we develop a structure correlation-based algorithm to evaluate the relationship among adjacent frames, where frames with similar structure property should have a strong correlation coefficient. Afterwards, a kernel multi-SVM is trained for video classification and high aesthetic video selection. Comprehensive experiments demonstrate the effectiveness of our method. |
| ArticleNumber | 102643 |
| Author | Liu, Sitong Zhang, Chao Li, Huizi |
| Author_xml | – sequence: 1 givenname: Chao surname: Zhang fullname: Zhang, Chao organization: School of Music and Recording Arts, Communication University of China, Beijing, China – sequence: 2 givenname: Sitong surname: Liu fullname: Liu, Sitong email: liusitong@guat.edu.cn organization: Guilin University of Aerospace Technology, Guilin, China – sequence: 3 givenname: Huizi surname: Li fullname: Li, Huizi organization: School of Music and Recording Arts, Communication University of China, Beijing, China |
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| Keywords | Video aesthetic assessment SVM Structure correlation |
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