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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3ED4F9S
Repositorysid.inpe.br/sibgrapi/2013/07.02.03.58
Last Update2013:07.02.03.58.55 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.02.03.58.55
Metadata Last Update2022:06.14.00.07.41 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.11
Citation KeyWangBoye:2013:FeLeMu
TitleFeature Learning by Multidimensional Scaling and its Applications in Object Recognition
FormatOn-line.
Year2013
Access Date2024, Apr. 28
Number of Files1
Size672 KiB
2. Context
Author1 Wang, Quan
2 Boyer, Kim L.
Affiliation1 Rensselaer Polytechnic Institute
2 Rensselaer Polytechnic Institute
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addresswangq10@rpi.edu
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-02 03:58:55 :: wangq10@rpi.edu -> administrator ::
2022-06-14 00:07:41 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsFeature learning
image distance measurement
multidimensional scaling
spatial pyramid matching
AbstractWe present the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pairwise image distances to learn fixed-length vector representations of images. The aspects of the images that are captured by the learned features, which we call MDS features, completely depend on what kind of image distance measurement is employed. With properly selected semantics-sensitive image distances, the MDS features provide rich semantic information about the images that is not captured by other feature extraction techniques. In our work, we introduce the iterated Levenberg-Marquardt algorithm for solving MDS, and study the MDS feature learning with IMage Euclidean Distance (IMED) and Spatial Pyramid Matching (SPM) distance. We present experiments on both synthetic data and real images the publicly accessible UIUC car image dataset. The MDS features based on SPM distance achieve exceptional performance for the car recognition task.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Feature Learning by...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Feature Learning by...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3ED4F9S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3ED4F9S
Languageen
Target FileMDS_SIBGRAPI_2013.pdf
User Groupwangq10@rpi.edu
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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