Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/29099
Title: | Deep Semi-Supervised Multi-Label image Classification |
Authors: | DEROUICHE, Chaima DEBBAGH Farah BENSALEM, Fatma Zohra |
Keywords: | MLC deep semi-supervised deep learning |
Issue Date: | Jul-2021 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
Abstract: | The multi-label image classification (MLC) is the process that aims to firstly learn
from training set of images, where each one can belong to multiple classes and so after
be able to predict more than one class label simultaneously for a new tested image. This
process suffers from several problems such as overlapping meaning that may contain
image labels. In this thesis, we present a Deep Semi-supervised multi-label image
classification method that compose modules: CAE module and ResNet module . The
first module consist of a CAE model that is used to extract the features of images. The
second one, is a ResNet is used to classify the extracted features.The proposed model
has been trained on public benchmark dataset and it achieves better results compared
to state of the art. The multi-label image classification (MLC) is the process that aims to firstly learn from training set of images, where each one can belong to multiple classes and so after be able to predict more than one class label simultaneously for a new tested image. This process suffers from several problems such as overlapping meaning that may contain image labels. In this thesis, we present a Deep Semi-supervised multi-label image classification method that compose modules: CAE module and ResNet module . The first module consist of a CAE model that is used to extract the features of images. The second one, is a ResNet is used to classify the extracted features.The proposed model has been trained on public benchmark dataset and it achieves better results compared to state of the art. The multi-label image classification (MLC) is the process that aims to firstly learn from training set of images, where each one can belong to multiple classes and so after be able to predict more than one class label simultaneously for a new tested image. This process suffers from several problems such as overlapping meaning that may contain image labels. In this thesis, we present a Deep Semi-supervised multi-label image classification method that compose modules: CAE module and ResNet module . The first module consist of a CAE model that is used to extract the features of images. The second one, is a ResNet is used to classify the extracted features.The proposed model has been trained on public benchmark dataset and it achieves better results compared to state of the art. |
URI: | http://dspace.univ-ouargla.dz/jspui/handle/123456789/29099 |
Appears in Collections: | Département d'informatique et technologie de l'information - Master |
Files in This Item:
File | Description | Size | Format | |
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Deep semi-supervised multi-label image classification.pdf | Fundamental Computer Science | 1,6 MB | Adobe PDF | View/Open |
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