Close
Metadata

Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4392S6S
Repositorysid.inpe.br/sibgrapi/2020/09.15.09.40
Last Update2020:09.15.09.40.40 administrator
Metadatasid.inpe.br/sibgrapi/2020/09.15.09.40.40
Metadata Last Update2020:10.29.22.04.53 administrator
Citation KeySilvaMeirSilv:2020:UsPaLe
TitleUsing Partial Least Squares in Butterfly Species Identification
FormatOn-line
Year2020
DateNov. 7-10, 2020
Access Date2021, Jan. 19
Number of Files1
Size1771 KiB
Context area
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 LocationVirtual
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2020-09-15 09:40:40 :: samirapgti@gmail.com -> administrator ::
2020-10-29 22:04:53 :: administrator -> samirapgti@gmail.com :: 2020
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
source Directory Contentthere are no files
agreement Directory Content
agreement.html 15/09/2020 06:40 1.2 KiB 
Conditions of access and use area
data URLhttp://urlib.net/rep/8JMKD3MGPEW34M/4392S6S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4392S6S
Languageen
Target Fileexample.pdf
User Groupsamirapgti@gmail.com
Visibilityshown
Update Permissionnot transferred
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
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
Description control area
e-Mail (login)samirapgti@gmail.com
update 

Close