Image annotation by search

Digital content have been gaining more attention in recent years, and digital images are becoming more popular as a means of personal recount of people’s life. To deal with the sudden influx of digital images, there is a need for automatic image annotation to automatically categorize the images via...

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Main Author: Cheng, Yin Hao
Other Authors: Xu Dong
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/62837
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-628372023-03-03T20:35:32Z Image annotation by search Cheng, Yin Hao Xu Dong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Digital content have been gaining more attention in recent years, and digital images are becoming more popular as a means of personal recount of people’s life. To deal with the sudden influx of digital images, there is a need for automatic image annotation to automatically categorize the images via their semantic content. This will allow people to maintain their digital images library easily, providing a user friendly platform for storage and sharing of personal memories. In this report, a 4-phrase methodology for image annotation was used. Each of the 4 phrases can be easily replaced or modified without affecting other phrases, much like the modular approach in software engineering design. Each of the phrases will target a specific area of the image annotation process.To achieve the best results, experimentations were done using the various extraction techniques in combination with the classification techniques. This allowed us to discover the technique or combination of techniques that will obtain the highest accuracy in terms of image annotation. The results yielded from the experimentations indicated that the best combination of techniques is the use of Dense SIFT (Level 1) with Bag of keypoints and classified via Linear Support Vector Machine (SVM). The relatively faster speed of this combination as compared with other technique combinations enable this technique combination to have the best of both worlds, namely accuracy and processing speed. Bachelor of Engineering (Computer Science) 2015-04-29T09:13:21Z 2015-04-29T09:13:21Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62837 en Nanyang Technological University 49 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::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Cheng, Yin Hao
Image annotation by search
description Digital content have been gaining more attention in recent years, and digital images are becoming more popular as a means of personal recount of people’s life. To deal with the sudden influx of digital images, there is a need for automatic image annotation to automatically categorize the images via their semantic content. This will allow people to maintain their digital images library easily, providing a user friendly platform for storage and sharing of personal memories. In this report, a 4-phrase methodology for image annotation was used. Each of the 4 phrases can be easily replaced or modified without affecting other phrases, much like the modular approach in software engineering design. Each of the phrases will target a specific area of the image annotation process.To achieve the best results, experimentations were done using the various extraction techniques in combination with the classification techniques. This allowed us to discover the technique or combination of techniques that will obtain the highest accuracy in terms of image annotation. The results yielded from the experimentations indicated that the best combination of techniques is the use of Dense SIFT (Level 1) with Bag of keypoints and classified via Linear Support Vector Machine (SVM). The relatively faster speed of this combination as compared with other technique combinations enable this technique combination to have the best of both worlds, namely accuracy and processing speed.
author2 Xu Dong
author_facet Xu Dong
Cheng, Yin Hao
format Final Year Project
author Cheng, Yin Hao
author_sort Cheng, Yin Hao
title Image annotation by search
title_short Image annotation by search
title_full Image annotation by search
title_fullStr Image annotation by search
title_full_unstemmed Image annotation by search
title_sort image annotation by search
publishDate 2015
url http://hdl.handle.net/10356/62837
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