Sign Language Spotting with a Threshold Model Based on Conditional Random Fields.
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| Title: | Sign Language Spotting with a Threshold Model Based on Conditional Random Fields. |
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| Authors: | Hee-Deok Yang1 hdyang@image.korea.ac.kr, Stan Sclaroff2 sclaroff@cs.bu.edu, Seong-Whan Lee1 swlee@image.korea.ac.kr |
| Source: | IEEE Transactions on Pattern Analysis & Machine Intelligence. Jul2009, Vol. 31 Issue 7, p1264-1277. 14p. 11 Diagrams, 10 Charts. |
| Subjects: | Sign language, Stochastic processes, Engineering instruments, Language & languages, Vocabulary, Detectors |
| Abstract: | Sign language spotting is the task of detecting and recognizing signs in a signed utterance, in a set vocabulary. The difficulty of sign language spotting is that instances of signs vary in both motion and appearance. Moreover, signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and nonsign patterns (which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns. A short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experiments demonstrate that our system can spot signs from continuous data with an 87.0 percent spotting rate and can recognize signs from isolated data with a 93.5 percent recognition rate versus 73.5 percent and 85.4 percent, respectively, for CRFs without a threshold model, short-sign detection, subsign reasoning, and hand appearance-based sign verification. Our system can also achieve a 15.0 percent sign error rate (SER) from continuous data and a 6.4 percent SER from isolated data versus 76.2 percent and 14.5 percent, respectively, for conventional CRFs. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence 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: 42310043 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sign Language Spotting with a Threshold Model Based on Conditional Random Fields. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hee-Deok+Yang%22">Hee-Deok Yang</searchLink><relatesTo>1</relatesTo><i> hdyang@image.korea.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Stan+Sclaroff%22">Stan Sclaroff</searchLink><relatesTo>2</relatesTo><i> sclaroff@cs.bu.edu</i><br /><searchLink fieldCode="AR" term="%22Seong-Whan+Lee%22">Seong-Whan Lee</searchLink><relatesTo>1</relatesTo><i> swlee@image.korea.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Pattern+Analysis+%26+Machine+Intelligence%22">IEEE Transactions on Pattern Analysis & Machine Intelligence</searchLink>. Jul2009, Vol. 31 Issue 7, p1264-1277. 14p. 11 Diagrams, 10 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sign+language%22">Sign language</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+instruments%22">Engineering instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary%22">Vocabulary</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Sign language spotting is the task of detecting and recognizing signs in a signed utterance, in a set vocabulary. The difficulty of sign language spotting is that instances of signs vary in both motion and appearance. Moreover, signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and nonsign patterns (which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns. A short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experiments demonstrate that our system can spot signs from continuous data with an 87.0 percent spotting rate and can recognize signs from isolated data with a 93.5 percent recognition rate versus 73.5 percent and 85.4 percent, respectively, for CRFs without a threshold model, short-sign detection, subsign reasoning, and hand appearance-based sign verification. Our system can also achieve a 15.0 percent sign error rate (SER) from continuous data and a 6.4 percent SER from isolated data versus 76.2 percent and 14.5 percent, respectively, for conventional CRFs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence 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/TPAMI.2008.172 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1264 Subjects: – SubjectFull: Sign language Type: general – SubjectFull: Stochastic processes Type: general – SubjectFull: Engineering instruments Type: general – SubjectFull: Language & languages Type: general – SubjectFull: Vocabulary Type: general – SubjectFull: Detectors Type: general Titles: – TitleFull: Sign Language Spotting with a Threshold Model Based on Conditional Random Fields. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hee-Deok Yang – PersonEntity: Name: NameFull: Stan Sclaroff – PersonEntity: Name: NameFull: Seong-Whan Lee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 01628828 Numbering: – Type: volume Value: 31 – Type: issue Value: 7 Titles: – TitleFull: IEEE Transactions on Pattern Analysis & Machine Intelligence Type: main |
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