Landmark recognition using deep learning
Large-scale picture retrieval is an important task in computer vision since it is linked to a variety of practical applications, such as object detection, visual place recognition, and product recognition. With applications in search, image understanding, apps, maps, medical, drones, and self-...
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sg-ntu-dr.10356-1580112023-07-07T19:14:49Z Landmark recognition using deep learning Ye, Lin Ko Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Electrical and electronic engineering Large-scale picture retrieval is an important task in computer vision since it is linked to a variety of practical applications, such as object detection, visual place recognition, and product recognition. With applications in search, image understanding, apps, maps, medical, drones, and self-driving automobiles, computer vision has become omnipresent in our communities. Visual recognition tasks including image classification, localization, and detection are at the heart of many of these applications. Among all these practical applications, image classification for landmarks will be focus on this project. The solution for this project is to train the identical dataset with different classes to study the accuracy of the various model. In order to have better accuracy of the landmarks’ recognition, advanced algorithms are required to develop in order to train the model with big datasets. In this project, it aims to create a python-based model to classify landmark images with an appropriate label for model development and study how the different classes affect the result of the model. This can help a lot of people to recognize the landmark photos which has been taken and organize their photo collection with correct label. The report concludes with some discussion of the project's outcomes as well as suggestions for improvements. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-26T13:46:35Z 2022-05-26T13:46:35Z 2022 Final Year Project (FYP) Ye, L. K. (2022). Landmark recognition using deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158011 https://hdl.handle.net/10356/158011 en P3037-202 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Ye, Lin Ko Landmark recognition using deep learning |
description |
Large-scale picture retrieval is an important task in computer vision since it is
linked to a variety of practical applications, such as object detection, visual place
recognition, and product recognition. With applications in search, image understanding,
apps, maps, medical, drones, and self-driving automobiles, computer vision has become
omnipresent in our communities. Visual recognition tasks including image classification,
localization, and detection are at the heart of many of these applications. Among all these
practical applications, image classification for landmarks will be focus on this project. The
solution for this project is to train the identical dataset with different classes to study the
accuracy of the various model. In order to have better accuracy of the landmarks’
recognition, advanced algorithms are required to develop in order to train the model with
big datasets.
In this project, it aims to create a python-based model to classify landmark images with
an appropriate label for model development and study how the different classes affect the
result of the model.
This can help a lot of people to recognize the landmark photos which has been taken and
organize their photo collection with correct label. The report concludes with some
discussion of the project's outcomes as well as suggestions for improvements. |
author2 |
Yap Kim Hui |
author_facet |
Yap Kim Hui Ye, Lin Ko |
format |
Final Year Project |
author |
Ye, Lin Ko |
author_sort |
Ye, Lin Ko |
title |
Landmark recognition using deep learning |
title_short |
Landmark recognition using deep learning |
title_full |
Landmark recognition using deep learning |
title_fullStr |
Landmark recognition using deep learning |
title_full_unstemmed |
Landmark recognition using deep learning |
title_sort |
landmark recognition using deep learning |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158011 |
_version_ |
1772826358048620544 |