| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | sibgrapi.sid.inpe.br |
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
| Identifier | 8JMKD3MGPBW4/363S8PB |
| Repository | sid.inpe.br/sibgrapi@80/2009/09.16.00.15 |
| Last Update | 2009:09.16.00.15.07 (UTC) administrator |
| Metadata Repository | sid.inpe.br/sibgrapi@80/2009/09.16.00.15.08 |
| Metadata Last Update | 2022:06.14.00.14.10 (UTC) administrator |
| DOI | 10.1109/SIBGRAPI.2009.42 |
| Citation Key | SchwartzDavi:2009:LeDiAp |
| Title | Learning Discriminative Appearance-Based Models Using Partial Least Squares  |
| Format | Printed, On-line. |
| Year | 2009 |
| Access Date | 2024, May 02 |
| Number of Files | 1 |
| Size | 2383 KiB |
| 2. Context | |
| Author | 1 Schwartz, William Robson 2 Davis, Larry S. |
| Affiliation | 1 University of Maryland 2 University of Maryland |
| Editor | Nonato, Luis Gustavo Scharcanski, Jacob |
| e-Mail Address | schwartz@cs.umd.edu |
| Conference Name | Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI) |
| Conference Location | Rio de Janeiro, RJ, Brazil |
| Date | 11-14 Oct. 2009 |
| Publisher | IEEE Computer Society |
| Publisher City | Los Alamitos |
| Book Title | Proceedings |
| Tertiary Type | Full Paper |
| History (UTC) | 2010-08-28 20:03:28 :: schwartz@cs.umd.edu -> administrator :: 2022-06-14 00:14:10 :: administrator -> :: 2009 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Version Type | finaldraft |
| Keywords | Partial least squares PLS appearance-based recognition co-occurrence matrix HOG |
| Abstract | Appearance information is essential for applications such as tracking and people recognition. One of the main problems of using appearance-based discriminative models is the ambiguities among classes when the number of persons being considered increases. To reduce the amount of ambiguity, we propose the use of a rich set of feature descriptors based on color, textures and edges. Another issue regarding appearance modeling is the limited number of training samples available for each appearance. The discriminative models are created using a powerful statistical tool called Partial Least Squares (PLS), responsible for weighting the features according to their discriminative power for each different appearance. The experimental results, based on appearance-based person recognition, demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques. |
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2009 > Learning Discriminative Appearance-Based... |
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Learning Discriminative Appearance-Based... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| data URL | http://urlib.net/ibi/8JMKD3MGPBW4/363S8PB |
| zipped data URL | http://urlib.net/zip/8JMKD3MGPBW4/363S8PB |
| Language | en |
| Target File | paper_CameraReady.pdf |
| User Group | schwartz@cs.umd.edu |
| Visibility | shown |
| 5. Allied materials | |
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
| Next Higher Units | 8JMKD3MGPEW34M/46SJQ2S 8JMKD3MGPEW34M/4742MCS |
| Citing Item List | sid.inpe.br/sibgrapi/2022/05.14.19.43 2 |
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
| 6. Notes | |
| Empty Fields | archivingpolicy 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 |