Visual search and applications using convolutional neural networks

Convolutional Neural Networks (CNN) is one of the most prominent deep learning architecture in performing large scale visual intelligent tasks. CNN can be used for many purposes based on the data given. One of the purpose is to recognise tattoo images to assist in better law enforcement process. Tat...

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Main Author: Tanojo, Hosiana Elvirya
Other Authors: Yap Kim Hui
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/74677
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-746772023-07-07T17:14:54Z Visual search and applications using convolutional neural networks Tanojo, Hosiana Elvirya Yap Kim Hui School of Electrical and Electronic Engineering DRNTU::Engineering Convolutional Neural Networks (CNN) is one of the most prominent deep learning architecture in performing large scale visual intelligent tasks. CNN can be used for many purposes based on the data given. One of the purpose is to recognise tattoo images to assist in better law enforcement process. Tattoo has been used to identify criminal suspects because it is a biometric characteristic that makes it easier to narrow down and identify victims. This project focuses on developing image recognition application by using CNN on tattoo images dataset, particularly on tattoo classification and detection. Classification will identify if there is any tattoo exists in an image and detection will locate the identified tattoo. A total of 2000 images were used to train the network by using CNN and Fast R- CNN code for classification and detection respectively. Tattoo classification achieved accuracy of 93.33% and tattoo detection achieved mean Average Precision (mAP) or Intersection over Union (IoU) value of 70%. Both results are promising and achieved generally higher accuracy than similar research or standard. Future research can be done to enhance both the classification and detection feature. Bachelor of Engineering 2018-05-23T02:29:58Z 2018-05-23T02:29:58Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74677 en Nanyang Technological University 48 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Tanojo, Hosiana Elvirya
Visual search and applications using convolutional neural networks
description Convolutional Neural Networks (CNN) is one of the most prominent deep learning architecture in performing large scale visual intelligent tasks. CNN can be used for many purposes based on the data given. One of the purpose is to recognise tattoo images to assist in better law enforcement process. Tattoo has been used to identify criminal suspects because it is a biometric characteristic that makes it easier to narrow down and identify victims. This project focuses on developing image recognition application by using CNN on tattoo images dataset, particularly on tattoo classification and detection. Classification will identify if there is any tattoo exists in an image and detection will locate the identified tattoo. A total of 2000 images were used to train the network by using CNN and Fast R- CNN code for classification and detection respectively. Tattoo classification achieved accuracy of 93.33% and tattoo detection achieved mean Average Precision (mAP) or Intersection over Union (IoU) value of 70%. Both results are promising and achieved generally higher accuracy than similar research or standard. Future research can be done to enhance both the classification and detection feature.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Tanojo, Hosiana Elvirya
format Final Year Project
author Tanojo, Hosiana Elvirya
author_sort Tanojo, Hosiana Elvirya
title Visual search and applications using convolutional neural networks
title_short Visual search and applications using convolutional neural networks
title_full Visual search and applications using convolutional neural networks
title_fullStr Visual search and applications using convolutional neural networks
title_full_unstemmed Visual search and applications using convolutional neural networks
title_sort visual search and applications using convolutional neural networks
publishDate 2018
url http://hdl.handle.net/10356/74677
_version_ 1772826171322400768