| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | plutao.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W/3FCLN2N |
| Repository | sid.inpe.br/plutao/2013/12.12.18.59.44 (restricted access) |
| Last Update | 2014:01.17.13.02.21 (UTC) administrator |
| Metadata Repository | sid.inpe.br/plutao/2013/12.12.18.59.45 |
| Metadata Last Update | 2021:03.05.23.11.17 (UTC) administrator |
| ISSN | 1807-4545 |
| Label | lattes: 8594179234801599 3 PantaleãoDutrSand:2013:ScAnIm |
| Citation Key | PantaleãoDutrSand:2012:ScAnIm |
| Title | Scenario analysis for image classification using multi-objective optimization  |
| Year | 2012 |
| Month | set.-dez. |
| Access Date | 2024, May 20 |
| Secondary Type | PRE PN |
| Number of Files | 1 |
| Size | 1607 KiB |
| 2. Context | |
| Author | 1 Pantaleão, Eliana 2 Dutra, Luciano Vieira 3 Sandri, Sandra Aparecida |
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHMA |
| Group | 1 2 DPI-OBT-INPE-MCTI-GOV-BR 3 LAC-CTE-INPE-MCTI-GOV-BR |
| Affiliation | 1 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) |
| Author e-Mail Address | 1 2 3 sandri.at.lac.inpe.br@gmail.com |
| e-Mail Address | sandri.at.lac.inpe.br@gmail.com |
| Journal | InfoComp |
| Volume | 11 |
| Number | 3 |
| Pages | 15-22 |
| Secondary Mark | C_CIÊNCIA_DA_COMPUTAÇÃO C_CIÊNCIAS_AGRÁRIAS_I B5_CIÊNCIAS_BIOLÓGICAS_I B5_ENGENHARIAS_III B5_ENGENHARIAS_IV B3_INTERDISCIPLINAR B4_MATERIAIS |
| History (UTC) | 2013-12-12 18:59:45 :: lattes -> administrator :: 2014-01-17 13:02:22 :: administrator :: 2013 -> 2012 2021-03-05 23:11:17 :: administrator -> marcelo.pazos@inpe.br :: 2012 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | In a typical image classification task the analyst decides beforehand the number of classes and which image channels to use. If there is a need to modify the classes or data channels it is necessary to start over. This paper proposes a scenario analysis tool for the task of image classification as a way of automating this process. Each scenario represents the parameters that will be used in a complete supervised classification task including training and classification. The proposed method uses multi-objective optimization to evaluate different sets of attributes and classes and presents the compromising solutions regarding the user objectives. A class hierarchy structure is used to generate different class sets and the system attempts to find the most appropriate combinations of class and attribute sets. In this work the system is applied to remote sensing problems and we consider three objectives: the best classification accuracy the smallest attribute set and the biggest class set. The system shows the compromising combinations of class and attribute sets along with the accuracy on a testing sample. The user can then choose which combination to use for the image classification |
| Abstract | In a typical image classification task, the analyst decides beforehand the number of classes and which image channels to use. If there is a need to modify the classes or data channels, it is necessary to start over. This paper proposes a scenario analysis tool for the task of image classification as a way of automating this process. Each scenario represents the parameters that will be used in a complete supervised classification task, including training and classification. The proposed method uses multi-objective optimization to evaluate different sets of attributes and classes, and presents the compromising solutions, regarding the user objectives. A class hierarchy structure is used to generate different class sets, and the system attempts to find the most appropriate combinations of class and attribute sets. In this work, the system is applied to remote sensing problems and we consider three objectives: the best classification accuracy, the smallest attribute set and the biggest class set. The system shows the compromising combinations of class and attribute sets, along with the accuracy on a testing sample. The user can then choose which combination to use for the image classification. |
| Area | SRE |
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Scenario analysis for... |
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Scenario analysis for... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| Language | pt |
| User Group | lattes marcelo.pazos@inpe.br |
| Reader Group | administrator marcelo.pazos@inpe.br |
| Visibility | shown |
| Archiving Policy | denypublisher denyfinaldraft |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Mirror Repository | iconet.com.br/banon/2006/11.26.21.31 |
| Next Higher Units | 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3ESGTTP |
| Citing Item List | sid.inpe.br/mtc-m21/2012/07.13.14.53.50 5 sid.inpe.br/bibdigital/2013/09.09.15.05 4 sid.inpe.br/bibdigital/2013/09.22.23.14 4 |
| URL (untrusted data) | http://www.dcc.ufla.br/infocomp/index.php?option=com_content&view=article&id=530&Itemid=216 |
| Dissemination | WEBSCI; PORTALCAPES. |
| Host Collection | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notes | |
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi format isbn lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork |
| 7. Description control | |
| e-Mail (login) | marcelo.pazos@inpe.br |
| update | |