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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/4392S6S
Repositorysid.inpe.br/sibgrapi/2020/09.15.09.40
Last Update2020:09.15.09.40.40 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.15.09.40.40
Metadata Last Update2022:06.14.00.00.03 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00047
Citation KeySilvaMeirSilv:2020:UsPaLe
TitleUsing Partial Least Squares in Butterfly Species Identification
FormatOn-line
Year2020
Access Date2024, Sep. 16
Number of Files1
Size1771 KiB
2. Context
Author1 Silva, Alexandre
2 Meireles, Sincler
3 Silva, Samira
Affiliation1 Universidade do Estado de Minas Gerais
2 Universidade do Estado de Minas Gerais
3 Universidade do Estado de Minas Gerais
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addresssamirapgti@gmail.com
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-15 09:40:40 :: samirapgti@gmail.com -> administrator ::
2022-06-14 00:00:03 :: administrator -> samirapgti@gmail.com :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsButterfly Identification
Pattern Recognition
Partial Least Squares
AbstractButterflies are important insects in nature, and along with moths constitute the Lepidoptera order. At the global level, the number of existing butterfly species is approximately 16,000. Therefore, the identification of their species in images by humans consists in a laborious task. In this paper, we propose a novel approach to recognize butterfly species in images by combining handcrafted descriptors and the Partial Last Squares (PLS) algorithm. A set of PLS models are trained using an one-against-all protocol. The test phase consists in presenting images to all classifiers and the one which provides the highest response value contains in the positive set the predicted class. The performance of the proposed approach is evaluated on the Leeds Butterfly dataset. Experiments were conducted using HOG and LBP descriptors, separately and combined. The approach using HOG singly reported an accuracy rate of 68.72%, while using only LBP resulted in an accuracy rate of 77.33\%. Combining both descriptors this value changes to 76.27%. The proposed approach achieves the best results in all three versions when compared to state-of-the-art approaches. Experiments have shown that describing images with LBP provides the highest accuracy values since it extracts texture information, what is an important characteristic to distinguish butterflies. However, information of color and shape, added by HOG, appears to make different species confused.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Using Partial Least...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/4392S6S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4392S6S
Languageen
Target Fileexample.pdf
User Groupsamirapgti@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 28
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy 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
7. Description control
e-Mail (login)samirapgti@gmail.com
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