Kernelized Multiview Projection for Robust Action Recognition.
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| Title: | Kernelized Multiview Projection for Robust Action Recognition. |
|---|---|
| Authors: | Shao, Ling1 ling.shao@ieee.org, Liu, Li1 li2.liu@northumbria.ac.uk, Yu, Mengyang1 m.y.yu@ieee.org |
| Source: | International Journal of Computer Vision. Jun2016, Vol. 118 Issue 2, p115-129. 15p. |
| Subjects: | Mathematical models of human behavior, Kernel (Mathematics), Kernel functions, Distance education research, Semantics, Mathematical models |
| Abstract: | Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP's performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision 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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| Items | – Name: Title Label: Title Group: Ti Data: Kernelized Multiview Projection for Robust Action Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shao%2C+Ling%22">Shao, Ling</searchLink><relatesTo>1</relatesTo><i> ling.shao@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Li%22">Liu, Li</searchLink><relatesTo>1</relatesTo><i> li2.liu@northumbria.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Mengyang%22">Yu, Mengyang</searchLink><relatesTo>1</relatesTo><i> m.y.yu@ieee.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Jun2016, Vol. 118 Issue 2, p115-129. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mathematical+models+of+human+behavior%22">Mathematical models of human behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Kernel+%28Mathematics%29%22">Kernel (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+education+research%22">Distance education research</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP's performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision 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/s11263-015-0861-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 115 Subjects: – SubjectFull: Mathematical models of human behavior Type: general – SubjectFull: Kernel (Mathematics) Type: general – SubjectFull: Kernel functions Type: general – SubjectFull: Distance education research Type: general – SubjectFull: Semantics Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Kernelized Multiview Projection for Robust Action Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shao, Ling – PersonEntity: Name: NameFull: Liu, Li – PersonEntity: Name: NameFull: Yu, Mengyang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 118 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Computer Vision Type: main |
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