Web video thumbnail recommendation with content-aware analysis and query-sensitive matching.
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| Title: | Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. |
|---|---|
| Authors: | Zhang, Weigang1 wgzhang@hit.edu.cn, Liu, Chunxi2, Wang, Zhenjun2, Li, Guorong2, Huang, Qingming qmhuang@jdl.ac.cn, Gao, Wen |
| Source: | Multimedia Tools & Applications. Nov2014, Vol. 73 Issue 1, p547-571. 25p. |
| Subjects: | Streaming video & television, Thumbnail images (Image processing), Image quality analysis, Support vector machines, Classification algorithms, Regression analysis |
| Abstract: | In this paper, a unified and adaptive web video thumbnail recommendation framework is proposed, which recommends thumbnails both for video owners and browsers on the basis of image quality assessment, image accessibility analysis, video content representativeness analysis and query-sensitive matching. At the very start, video shot detection is performed and the highest image quality video frame is extracted as the key frame for each shot on the basis of our proposed image quality assessment method. These key frames are utilized as the thumbnail candidates for the following processes. In the image quality assessment, the normalized variance autofocusing function is employed to evaluate the image blur and ensures that the selected video thumbnail candidates are clear and have high image quality. For accessibility analysis, color moment, visual salience and texture are used with a support vector regression model to predict the candidates' accessibility score, which ensures that the recommended thumbnail's ROIs are big enough and it is very accessible for users. For content representativeness analysis, the mutual reinforcement algorithm is adopted in the entire video to obtain the candidates' representativeness score, which ensures that the final thumbnail is representative enough for users to catch the main video contents at a glance. Considering browsers' query intent, a relevant model is designed to recommend more personalized thumbnails for certain browsers. Finally, by flexibly fusing the above analysis results, the final adaptive recommendation work is accomplished. Experimental results and subjective evaluations demonstrate the effectiveness of the proposed approach. Compared with the existing web video thumbnail generation methods, the thumbnails for video owners not only reflect the contents of the video better, but also make users feel more comfortable. The thumbnails for video browsers directly reflect their preference, which greatly enhances their user experience. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 98420239 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Weigang%22">Zhang, Weigang</searchLink><relatesTo>1</relatesTo><i> wgzhang@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chunxi%22">Liu, Chunxi</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhenjun%22">Wang, Zhenjun</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Guorong%22">Li, Guorong</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Qingming%22">Huang, Qingming</searchLink><i> qmhuang@jdl.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Wen%22">Gao, Wen</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Nov2014, Vol. 73 Issue 1, p547-571. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Streaming+video+%26+television%22">Streaming video & television</searchLink><br /><searchLink fieldCode="DE" term="%22Thumbnail+images+%28Image+processing%29%22">Thumbnail images (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, a unified and adaptive web video thumbnail recommendation framework is proposed, which recommends thumbnails both for video owners and browsers on the basis of image quality assessment, image accessibility analysis, video content representativeness analysis and query-sensitive matching. At the very start, video shot detection is performed and the highest image quality video frame is extracted as the key frame for each shot on the basis of our proposed image quality assessment method. These key frames are utilized as the thumbnail candidates for the following processes. In the image quality assessment, the normalized variance autofocusing function is employed to evaluate the image blur and ensures that the selected video thumbnail candidates are clear and have high image quality. For accessibility analysis, color moment, visual salience and texture are used with a support vector regression model to predict the candidates' accessibility score, which ensures that the recommended thumbnail's ROIs are big enough and it is very accessible for users. For content representativeness analysis, the mutual reinforcement algorithm is adopted in the entire video to obtain the candidates' representativeness score, which ensures that the final thumbnail is representative enough for users to catch the main video contents at a glance. Considering browsers' query intent, a relevant model is designed to recommend more personalized thumbnails for certain browsers. Finally, by flexibly fusing the above analysis results, the final adaptive recommendation work is accomplished. Experimental results and subjective evaluations demonstrate the effectiveness of the proposed approach. Compared with the existing web video thumbnail generation methods, the thumbnails for video owners not only reflect the contents of the video better, but also make users feel more comfortable. The thumbnails for video browsers directly reflect their preference, which greatly enhances their user experience. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature 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: 10.1007/s11042-013-1607-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 547 Subjects: – SubjectFull: Streaming video & television Type: general – SubjectFull: Thumbnail images (Image processing) Type: general – SubjectFull: Image quality analysis Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Regression analysis Type: general Titles: – TitleFull: Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Weigang – PersonEntity: Name: NameFull: Liu, Chunxi – PersonEntity: Name: NameFull: Wang, Zhenjun – PersonEntity: Name: NameFull: Li, Guorong – PersonEntity: Name: NameFull: Huang, Qingming – PersonEntity: Name: NameFull: Gao, Wen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 73 – Type: issue Value: 1 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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