Semantic Context-Aware Image Style Transfer.
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| Title: | Semantic Context-Aware Image Style Transfer. |
|---|---|
| Authors: | Liao, Yi-Sheng1 (AUTHOR) g107056049@mail.nchu.edu.tw, Huang, Chun-Rong1 (AUTHOR) crhuang@nchu.edu.tw |
| Source: | IEEE Transactions on Image Processing. 2022, Vol. 31, p1911-1923. 13p. |
| Subjects: | Image color analysis, Image registration |
| Abstract: | To provide semantic image style transfer results which are consistent with human perception, transferring styles of semantic regions of the style image to their corresponding semantic regions of the content image is necessary. However, when the object categories between the content and style images are not the same, it is difficult to match semantic regions between two images for semantic image style transfer. To solve the semantic matching problem and guide the semantic image style transfer based on matched regions, we propose a novel semantic context-aware image style transfer method by performing semantic context matching followed by a hierarchical local-to-global network architecture. The semantic context matching aims to obtain the corresponding regions between the content and style images by using context correlations of different object categories. Based on the matching results, we retrieve semantic context pairs where each pair is composed of two semantically matched regions from the content and style images. To achieve semantic context-aware style transfer, a hierarchical local-to-global network architecture, which contains two sub-networks including the local context network and the global context network, is proposed. The former focuses on style transfer for each semantic context pair from the style image to the content image, and generates a local style transfer image storing the detailed style feature representations for corresponding semantic regions. The latter aims to derive the stylized image by considering the content, the style, and the intermediate local style transfer images, so that inconsistency between different corresponding semantic regions can be addressed and solved. The experimental results show that the stylized results using our method are more consistent with human perception compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 170077131 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Semantic Context-Aware Image Style Transfer. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liao%2C+Yi-Sheng%22">Liao, Yi-Sheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> g107056049@mail.nchu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Chun-Rong%22">Huang, Chun-Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> crhuang@nchu.edu.tw</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. 2022, Vol. 31, p1911-1923. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+color+analysis%22">Image color analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To provide semantic image style transfer results which are consistent with human perception, transferring styles of semantic regions of the style image to their corresponding semantic regions of the content image is necessary. However, when the object categories between the content and style images are not the same, it is difficult to match semantic regions between two images for semantic image style transfer. To solve the semantic matching problem and guide the semantic image style transfer based on matched regions, we propose a novel semantic context-aware image style transfer method by performing semantic context matching followed by a hierarchical local-to-global network architecture. The semantic context matching aims to obtain the corresponding regions between the content and style images by using context correlations of different object categories. Based on the matching results, we retrieve semantic context pairs where each pair is composed of two semantically matched regions from the content and style images. To achieve semantic context-aware style transfer, a hierarchical local-to-global network architecture, which contains two sub-networks including the local context network and the global context network, is proposed. The former focuses on style transfer for each semantic context pair from the style image to the content image, and generates a local style transfer image storing the detailed style feature representations for corresponding semantic regions. The latter aims to derive the stylized image by considering the content, the style, and the intermediate local style transfer images, so that inconsistency between different corresponding semantic regions can be addressed and solved. The experimental results show that the stylized results using our method are more consistent with human perception compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TIP.2022.3149237 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1911 Subjects: – SubjectFull: Image color analysis Type: general – SubjectFull: Image registration Type: general Titles: – TitleFull: Semantic Context-Aware Image Style Transfer. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liao, Yi-Sheng – PersonEntity: Name: NameFull: Huang, Chun-Rong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 10577149 Numbering: – Type: volume Value: 31 Titles: – TitleFull: IEEE Transactions on Image Processing Type: main |
| ResultId | 1 |
