Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JM939P
Repositorysid.inpe.br/sibgrapi/2015/06.16.17.05
Last Update2015:06.16.17.05.18 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.16.17.05.18
Metadata Last Update2022:06.14.00.08.02 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.28
Citation KeyPedronetteTorr:2015:UnEfEs
TitleUnsupervised Effectiveness Estimation for Image Retrieval using Reciprocal Rank Information
FormatOn-line
Year2015
Access Date2024, May 05
Number of Files1
Size646 KiB
2. Context
Author1 Pedronette, Daniel Carlos Guimarães
2 Torres, Ricardo da S.
Affiliation1 State University of São Paulo (UNESP)
2 University of Campinas (UNICAMP)
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addresspedronette@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-16 17:05:18 :: pedronette@gmail.com -> administrator ::
2022-06-14 00:08:02 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscontent-based image retrieval
unsupervised effectiveness estimation
query difficult prediction
AbstractIn this paper, we present an unsupervised approach for estimating the effectiveness of image retrieval results obtained for a given query. The proposed approach does not require any training procedure and the computational efforts needed are very low, since only the top-k results are analyzed. In addition, we also discuss the use of the unsupervised measures in two novel rank aggregation methods, which assign weights to ranked lists according to their effectiveness estimation. An experimental evaluation was conducted considering different datasets and various image descriptors. Experimental results demonstrate the capacity of the proposed measures in correctly estimating the effectiveness of different queries in an unsupervised manner. The linear correlation between the proposed and widely used effectiveness evaluation measures achieves scores up to 0.86 for some descriptors.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > Unsupervised Effectiveness Estimation...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Unsupervised Effectiveness Estimation...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 16/06/2015 14:05 0.7 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JM939P
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JM939P
Languageen
Target FilePID3767775.pdf
User Grouppedronette@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 10
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


Close