Missing ingredients in optimising large-scale image retrieval with deep features
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Osman Tursun Thesis
(PDF 35MB)
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis applies advanced image processing and deep machine learning techniques to solve the challenges of large-scale image retrieval. Solutions are provided to overcome key obstacles in real-world large-scale image retrieval applications by introducing unique methods for making deep learning systems more reliable and efficient. The outcome of the research is useful for several image retrieval applications including patent search, and trademark and logo infringement analysis.
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| ID Code: | 227803 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Sridharan, Sridha, Fookes, Clinton, Denman, Simon, & Sivapalan, Sabesan |
| Keywords: | Image retrieval, Transfer Learning, Deep Learning, Knowledge Distillation, Attention, Test-time Data Augmentation, Text Removal |
| DOI: | 10.5204/thesis.eprints.227803 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
| Institution: | Queensland University of Technology |
| Deposited On: | 22 Feb 2022 00:41 |
| Last Modified: | 22 Feb 2022 00:41 |
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