Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPEW34M/4392S6S |
Repository | sid.inpe.br/sibgrapi/2020/09.15.09.40 |
Last Update | 2020:09.15.09.40.40 administrator |
Metadata | sid.inpe.br/sibgrapi/2020/09.15.09.40.40 |
Metadata Last Update | 2020:10.29.22.04.53 administrator |
Citation Key | SilvaMeirSilv:2020:UsPaLe |
Title | Using Partial Least Squares in Butterfly Species Identification  |
Format | On-line |
Year | 2020 |
Date | Nov. 7-10, 2020 |
Access Date | 2021, Jan. 19 |
Number of Files | 1 |
Size | 1771 KiB |
Context area | |
Author | 1 Silva, Alexandre 2 Meireles, Sincler 3 Silva, Samira |
Affiliation | 1 Universidade do Estado de Minas Gerais 2 Universidade do Estado de Minas Gerais 3 Universidade do Estado de Minas Gerais |
Editor | Musse, Soraia Raupp Cesar Junior, Roberto Marcondes Pelechano, Nuria Wang, Zhangyang (Atlas) |
e-Mail Address | samirapgti@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 33 (SIBGRAPI) |
Conference Location | Virtual |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2020-09-15 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 Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Butterfly Identification, Pattern Recognition, Partial Least Squares. |
Abstract | Butterflies 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 Content | there are no files |
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data URL | http://urlib.net/rep/8JMKD3MGPEW34M/4392S6S |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/4392S6S |
Language | en |
Target File | example.pdf |
User Group | samirapgti@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/43G4L9S |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber 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 |
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e-Mail (login) | samirapgti@gmail.com |
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