Sparse Output Coding for Scalable Visual Recognition.
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| Title: | Sparse Output Coding for Scalable Visual Recognition. |
|---|---|
| Authors: | Zhao, Bin1 binzhao@andrew.cmu.edu, Xing, Eric1 epxing@cs.cmu.edu |
| Source: | International Journal of Computer Vision. Aug2016, Vol. 119 Issue 1, p60-75. 16p. |
| Subjects: | Object recognition (Computer vision), Probabilistic inference, Decoding algorithms, Computer programming, Empirical research |
| Abstract: | Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach. [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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| Header | DbId: egs DbLabel: Engineering Source An: 116285794 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sparse Output Coding for Scalable Visual Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Bin%22">Zhao, Bin</searchLink><relatesTo>1</relatesTo><i> binzhao@andrew.cmu.edu</i><br /><searchLink fieldCode="AR" term="%22Xing%2C+Eric%22">Xing, Eric</searchLink><relatesTo>1</relatesTo><i> epxing@cs.cmu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Aug2016, Vol. 119 Issue 1, p60-75. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+inference%22">Probabilistic inference</searchLink><br /><searchLink fieldCode="DE" term="%22Decoding+algorithms%22">Decoding algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=116285794 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11263-015-0839-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 60 Subjects: – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Probabilistic inference Type: general – SubjectFull: Decoding algorithms Type: general – SubjectFull: Computer programming Type: general – SubjectFull: Empirical research Type: general Titles: – TitleFull: Sparse Output Coding for Scalable Visual Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Bin – PersonEntity: Name: NameFull: Xing, Eric IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 119 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Computer Vision Type: main |
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