Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces
Identifieur interne : 000940 ( Main/Exploration ); précédent : 000939; suivant : 000941Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces
Auteurs : Habibi Aghdam [Iran] ; Saeid Payvar [Iran]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: There are several feature extracting techniques which can produce a large feature space for a given image. It is clear that only small numbers of these features are appropriate to classify the objects. But selecting an appropriate feature vector from the large feature space is a hard optimization problem. In this paper we address this problem using the well known optimization technique called Simulated Annealing. Also we show that how this framework can be used to design the optimal 2D rectangular filter banks for Printed Persian and English numerals classification, Printed English letters classification, Eye, Lip and Face detection problems.
Url:
DOI: 10.1007/978-3-642-02611-9_31
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 000A92
- to stream Istex, to step Curation: 000A79
- to stream Istex, to step Checkpoint: 000462
- to stream Main, to step Merge: 000948
- to stream Main, to step Curation: 000940
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces</title><author><name sortKey="Aghdam, Habibi" sort="Aghdam, Habibi" uniqKey="Aghdam H" first="Habibi" last="Aghdam">Habibi Aghdam</name></author><author><name sortKey="Payvar, Saeid" sort="Payvar, Saeid" uniqKey="Payvar S" first="Saeid" last="Payvar">Saeid Payvar</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:21B62645C7312148802EE8C3F39E66F819AE3913</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1007/978-3-642-02611-9_31</idno><idno type="url">https://api.istex.fr/document/21B62645C7312148802EE8C3F39E66F819AE3913/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000A92</idno><idno type="wicri:Area/Istex/Curation">000A79</idno><idno type="wicri:Area/Istex/Checkpoint">000462</idno><idno type="wicri:doubleKey">0302-9743:2009:Aghdam H:novel:framework:for</idno><idno type="wicri:Area/Main/Merge">000948</idno><idno type="wicri:Area/Main/Curation">000940</idno><idno type="wicri:Area/Main/Exploration">000940</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces</title><author><name sortKey="Aghdam, Habibi" sort="Aghdam, Habibi" uniqKey="Aghdam H" first="Habibi" last="Aghdam">Habibi Aghdam</name><affiliation wicri:level="1"><country xml:lang="fr">Iran</country><wicri:regionArea>Computer Engineering Department, Amirkabir University of Technology</wicri:regionArea><wicri:noRegion>Amirkabir University of Technology</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Iran</country></affiliation></author><author><name sortKey="Payvar, Saeid" sort="Payvar, Saeid" uniqKey="Payvar S" first="Saeid" last="Payvar">Saeid Payvar</name><affiliation wicri:level="1"><country xml:lang="fr">Iran</country><wicri:regionArea>Computer Engineering Department, Islamic Azad University</wicri:regionArea><wicri:noRegion>Islamic Azad University</wicri:noRegion></affiliation><affiliation><wicri:noCountry code="no comma">E-mail: payvar@gmail.com</wicri:noCountry></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2009</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">21B62645C7312148802EE8C3F39E66F819AE3913</idno><idno type="DOI">10.1007/978-3-642-02611-9_31</idno><idno type="ChapterID">31</idno><idno type="ChapterID">Chap31</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: There are several feature extracting techniques which can produce a large feature space for a given image. It is clear that only small numbers of these features are appropriate to classify the objects. But selecting an appropriate feature vector from the large feature space is a hard optimization problem. In this paper we address this problem using the well known optimization technique called Simulated Annealing. Also we show that how this framework can be used to design the optimal 2D rectangular filter banks for Printed Persian and English numerals classification, Printed English letters classification, Eye, Lip and Face detection problems.</div></front></TEI><affiliations><list><country><li>Iran</li></country></list><tree><country name="Iran"><noRegion><name sortKey="Aghdam, Habibi" sort="Aghdam, Habibi" uniqKey="Aghdam H" first="Habibi" last="Aghdam">Habibi Aghdam</name></noRegion><name sortKey="Aghdam, Habibi" sort="Aghdam, Habibi" uniqKey="Aghdam H" first="Habibi" last="Aghdam">Habibi Aghdam</name><name sortKey="Payvar, Saeid" sort="Payvar, Saeid" uniqKey="Payvar S" first="Saeid" last="Payvar">Saeid Payvar</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000940 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000940 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= OcrV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:21B62645C7312148802EE8C3F39E66F819AE3913
|texte= Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces
}}
| This area was generated with Dilib version V0.6.32. |  |