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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master Thesis : Human-centered machine learning for peer-to-peer exchange

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Chapeau, Pierre ULiège
Promotor(s) : Marée, Raphaël ULiège
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17388
Details
Title : Master Thesis : Human-centered machine learning for peer-to-peer exchange
Translated title : [fr] Apprentissage automatique centré sur l'humain pour l'échange entre pairs
Author : Chapeau, Pierre ULiège
Date of defense  : 26-Jun-2023/27-Jun-2023
Advisor(s) : Marée, Raphaël ULiège
Committee's member(s) : Donnet, Benoît ULiège
Debruyne, Christophe ULiège
Language : English
Number of pages : 70
Keywords : [en] Machine learning
[en] Computer vision
[en] Shareish
[en] Book pictures
Discipline(s) : Engineering, computing & technology > Computer science
Name of the research project : Shareish
Target public : Researchers
Professionals of domain
Student
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] The Shareish platform project has been created to encourage mutual sharing of
goods in the context of gift economy and generalized exchange. One of the critical aspect of the platform is to localize items on a map and to provide them with a detailed
description. This however is time consuming and ungrateful to do by hand, to address
this problem the use of artificial intelligence (AI) and more specifically computer vision
has been explored and integrated in the back-end of the prototype platform. Previously, an item recognition network has been already deployed and the Tesseract optical
character recognition engine was used to provide experimental results. This work will
expand and refine the integration of machine learning in the platform by introducing
metrics, defining tasks, and comparing different networks.
Previous work has been done relative to AI integration for the website : an automatic
image tagger trained on ImageNet, where some preliminary metric have been computed, fully integrated in the website. Tesseract was also tested as text detection and
reading model, the formal comparison between this model and the other new models
proposed is one of the subject of this document.


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Author

  • Chapeau, Pierre ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Donnet, Benoît ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorithmique des grands systèmes
    ORBi View his publications on ORBi
  • Debruyne, Christophe ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Représentation et ingénierie des données
    ORBi View his publications on ORBi
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  • Total number of downloads 2










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