1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3M3PPPL |
Repository | sid.inpe.br/sibgrapi/2016/07.11.14.00 |
Last Update | 2016:07.11.14.00.03 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2016/07.11.14.00.03 |
Metadata Last Update | 2022:06.14.00.08.21 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2016.058 |
Citation Key | CamposMantJr:2016:MeApRe |
Title | A Meta-learning Approach for Recommendation of Image Segmentation Algorithms |
Format | On-line |
Year | 2016 |
Access Date | 2024, May 03 |
Number of Files | 1 |
Size | 11805 KiB |
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2. Context | |
Author | 1 Campos, Gabriel F. C. 2 Mantovani, Rafael G. 3 Jr., Sylvio Barbon |
Affiliation | 1 Londrina State University (UEL) 2 University of Sao Paulo (USP) 3 Londrina State University (UEL) |
Editor | Aliaga, Daniel G. Davis, Larry S. Farias, Ricardo C. Fernandes, Leandro A. F. Gibson, Stuart J. Giraldi, Gilson A. Gois, João Paulo Maciel, Anderson Menotti, David Miranda, Paulo A. V. Musse, Soraia Namikawa, Laercio Pamplona, Mauricio Papa, João Paulo Santos, Jefersson dos Schwartz, William Robson Thomaz, Carlos E. |
e-Mail Address | gabrielfcc@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 29 (SIBGRAPI) |
Conference Location | São José dos Campos, SP, Brazil |
Date | 4-7 Oct. 2016 |
Publisher | IEEE Computer Society´s Conference Publishing Services |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2016-07-11 14:00:03 :: gabrielfcc@gmail.com -> administrator :: 2016-10-05 14:49:10 :: administrator -> gabrielfcc@gmail.com :: 2016 2016-10-13 03:29:44 :: gabrielfcc@gmail.com -> administrator :: 2016 2022-06-14 00:08:21 :: administrator -> :: 2016 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Segmentation algorithm recommendation metalearning image processing |
Abstract | There are many algorithms for image segmentation, but there is no optimal algorithm for all kind of image applications. To recommend an adequate algorithm for segmentation is a challenging task that requires knowledge about the problem and algorithms. Meta-learning has recently emerged from machine learning research field to solve the algorithm selection problem. This paper applies meta-learning to recommend segmentation algorithms based on meta-knowledge. We performed experiments in four different meta-databases representing various real world problems, recommending when three different segmentation techniques are adequate or not. A set of 44 features based on color, frequency domain, histogram, texture, contrast and image quality were extracted from images, obtaining enough discriminative power for the recommending task in different segmentation scenarios. Results show that Random Forest meta-models were able to recommend segmentation algorithms with high predictive performance. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2016 > A Meta-learning Approach... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > A Meta-learning Approach... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3M3PPPL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3M3PPPL |
Language | en |
Target File | PID4348117.pdf |
User Group | gabrielfcc@gmail.com |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3M2D4LP 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2016/07.02.23.50 5 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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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