Multi-classifire scheme for adult image classification with low-level visual features

Multi-classifire scheme for adult image classification with low-level visual features

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Bibliographic Details
Main Author: Bozorgi, Mohammadmehdi
Format: Thesis
Published: 2011
Subjects:
QA75 Electronic computers
Computer science
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id my-utm-ep.26863
record_format uketd_dc
spelling my-utm-ep.268632017-07-10T07:23:01Z Multi-classifire scheme for adult image classification with low-level visual features 2011 Bozorgi, Mohammadmehdi QA75 Electronic computers. Computer science 2011 Thesis http://eprints.utm.my/id/eprint/26863/ http://libraryopac.utm.my/client/en_AU/main/search/results?qu=Multi-classifire+scheme+for+adult+image+classification+with+low-level+visual+features&te= masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Bozorgi, Mohammadmehdi
Multi-classifire scheme for adult image classification with low-level visual features
description
format Thesis
qualification_level Master's degree
author Bozorgi, Mohammadmehdi
author_facet Bozorgi, Mohammadmehdi
author_sort Bozorgi, Mohammadmehdi
title Multi-classifire scheme for adult image classification with low-level visual features
title_short Multi-classifire scheme for adult image classification with low-level visual features
title_full Multi-classifire scheme for adult image classification with low-level visual features
title_fullStr Multi-classifire scheme for adult image classification with low-level visual features
title_full_unstemmed Multi-classifire scheme for adult image classification with low-level visual features
title_sort multi-classifire scheme for adult image classification with low-level visual features
granting_institution Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department Faculty of Computer Science and Information System
publishDate 2011
_version_ 1747815529094053888

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