Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection.
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| Title: | Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection. |
|---|---|
| Authors: | Yu, Mengzhu1,2, Tang, Zhenjun1,2 zjtang@gxnu.edu.cn, Zhang, Hanyun1,2, Liang, Xiaoping1,2, Zhang, Xianquan1,2 |
| Source: | Computer Journal. Jun2024, Vol. 67 Issue 6, p2162-2171. 10p. |
| Subjects: | Hashing, Multiple correspondence analysis (Statistics), Histograms, Optical flow, Data mining |
| Abstract: | This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Journal is the property of Oxford University Press / USA 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: 178338262 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Mengzhu%22">Yu, Mengzhu</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Tang%2C+Zhenjun%22">Tang, Zhenjun</searchLink><relatesTo>1,2</relatesTo><i> zjtang@gxnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hanyun%22">Zhang, Hanyun</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Liang%2C+Xiaoping%22">Liang, Xiaoping</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xianquan%22">Zhang, Xianquan</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. Jun2024, Vol. 67 Issue 6, p2162-2171. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hashing%22">Hashing</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+correspondence+analysis+%28Statistics%29%22">Multiple correspondence analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Histograms%22">Histograms</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+flow%22">Optical flow</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA 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: 10.1093/comjnl/bxad130 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2162 Subjects: – SubjectFull: Hashing Type: general – SubjectFull: Multiple correspondence analysis (Statistics) Type: general – SubjectFull: Histograms Type: general – SubjectFull: Optical flow Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Mengzhu – PersonEntity: Name: NameFull: Tang, Zhenjun – PersonEntity: Name: NameFull: Zhang, Hanyun – PersonEntity: Name: NameFull: Liang, Xiaoping – PersonEntity: Name: NameFull: Zhang, Xianquan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00104620 Numbering: – Type: volume Value: 67 – Type: issue Value: 6 Titles: – TitleFull: Computer Journal Type: main |
| ResultId | 1 |
