Close
Metadata

Reference TypeConference Proceedings
Identifier8JMKD3MGPEW34M/4392S6S
Repositorysid.inpe.br/sibgrapi/2020/09.15.09.40
Metadatasid.inpe.br/sibgrapi/2020/09.15.09.40.40
Sitesibgrapi.sid.inpe.br
Citation KeySilvaMeirSilv:2020:UsPaLe
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
TitleUsing Partial Least Squares in Butterfly Species Identification
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Year2020
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
Book TitleProceedings
DateNov. 7-10, 2020
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationVirtual
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.
Languageen
Tertiary TypeFull Paper
FormatOn-line
Size1771 KiB
Number of Files1
Target Fileexample.pdf
Last Update2020:09.15.09.40.40 sid.inpe.br/banon/2001/03.30.15.38 administrator
Metadata Last Update2020:10.29.22.04.53 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2020}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addresssamirapgti@gmail.com
e-Mail (login)samirapgti@gmail.com
User Groupsamirapgti@gmail.com
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
Document Stagenot transferred
Next Higher Units8JMKD3MGPEW34M/43G4L9S
source Directory Contentthere are no files
agreement Directory Content
agreement.html 15/09/2020 06:40 1.2 KiB 
History2020-09-15 09:40:40 :: samirapgti@gmail.com -> administrator ::
2020-10-29 22:04:53 :: administrator -> samirapgti@gmail.com :: 2020
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2020, Nov. 28
update 

Close