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-...

Full description

Saved in:
Bibliographic Details
Main Author: Ye, Lin Ko
Other Authors: Yap Kim Hui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158011
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-158011
record_format dspace
spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle 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