Please use this identifier to cite or link to this item:
http://dspace.univ-tiaret.dz:80/handle/123456789/5324
Title: | Le deep learning pour la classification des images dans différents système de couleur. |
Authors: | Hatem, M’hamed Amine |
Keywords: | Deep learning, Color-spaces, Convolutional Neural Networks, ResNet, CapsuleNet |
Issue Date: | 2019 |
Publisher: | Université Ibn Khaldoun -Tiaret- |
Abstract: | Image classification is fundamental in the field of artificial intelligence, recently Deep Residual Learning [1] and the new coming model CapsulNet[2] have shown state-of-the-art performance for image classification tasks , they takes data-sets mostly as input in the form of RGB images even though we have many other colorspaces available. In this thesis we try to understand the impact of image color-space on the performance of CNN models in Image classification . We evaluate this on CIFAR10 [3]data-set by converting it into five other color-spaces HLS, HSV, LUV, LAB, YUV and trained each one of them in two different deep learning architecture models namely ResNet20 and CapsulNet , the results obtained show a minor change in accuracy but even a small percentage may make the difference, in the other hand LUV is good alternative it show improvement about 0.92% comparison to RGB in ResNet20 and 0.39% in CapsulNet. |
URI: | http://dspace.univ-tiaret.dz:80/handle/123456789/5324 |
Appears in Collections: | Master |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
TH.M.INF.FR.2019.26.pdf | 3,13 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.