Learning Where to Attend with Deep Architectures for Image Tracking.
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| Title: | Learning Where to Attend with Deep Architectures for Image Tracking. |
|---|---|
| Authors: | Denil, Misha, Bazzani, Loris, Larochelle, Hugo, de Freitas, Nando |
| Source: | Neural Computation. Aug2012, Vol. 24 Issue 8, p2151-2184. 34p. |
| Subjects: | Machine learning, Computer architecture, Statistical methods in image analysis, Neurosciences, Boltzmann machine, Ranking (Statistics), Performance evaluation |
| Abstract: | We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended tomirror the what andwhere pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press 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: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 76625570 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning Where to Attend with Deep Architectures for Image Tracking. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Denil%2C+Misha%22">Denil, Misha</searchLink><br /><searchLink fieldCode="AR" term="%22Bazzani%2C+Loris%22">Bazzani, Loris</searchLink><br /><searchLink fieldCode="AR" term="%22Larochelle%2C+Hugo%22">Larochelle, Hugo</searchLink><br /><searchLink fieldCode="AR" term="%22de+Freitas%2C+Nando%22">de Freitas, Nando</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Aug2012, Vol. 24 Issue 8, p2151-2184. 34p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+architecture%22">Computer architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+methods+in+image+analysis%22">Statistical methods in image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Boltzmann+machine%22">Boltzmann machine</searchLink><br /><searchLink fieldCode="DE" term="%22Ranking+%28Statistics%29%22">Ranking (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended tomirror the what andwhere pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press 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: 10.1162/NECO_a_00312 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 2151 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Computer architecture Type: general – SubjectFull: Statistical methods in image analysis Type: general – SubjectFull: Neurosciences Type: general – SubjectFull: Boltzmann machine Type: general – SubjectFull: Ranking (Statistics) Type: general – SubjectFull: Performance evaluation Type: general Titles: – TitleFull: Learning Where to Attend with Deep Architectures for Image Tracking. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Denil, Misha – PersonEntity: Name: NameFull: Bazzani, Loris – PersonEntity: Name: NameFull: Larochelle, Hugo – PersonEntity: Name: NameFull: de Freitas, Nando IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 24 – Type: issue Value: 8 Titles: – TitleFull: Neural Computation Type: main |
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