Image annotation by search

The recent advances in technology have led to an exponential growth in the number of digital images being stored on the Internet as well as in personal computers. Effective methods to organize and index photos based on semantic content have become essential to provide users with the conveni...

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Main Author: Luong, Phuoc Thanh.
Other Authors: Xu Dong
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/44873
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-448732023-03-03T20:43:58Z Image annotation by search Luong, Phuoc Thanh. Xu Dong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval The recent advances in technology have led to an exponential growth in the number of digital images being stored on the Internet as well as in personal computers. Effective methods to organize and index photos based on semantic content have become essential to provide users with the convenience of searching their albums for specific content without prior manual annotation. However, querying for the image content is still a challenging task which has attracted much research effort. In this paper, we present a photo query framework based on prior annotation. When the user provides a text query (e.g. “water”), the framework performs a search within the annotation database and finds relevant photos. To accomplish this goal, we built a set of classifiers to annotate user photos in advance, and used these annotations for query. We leveraged the NUS-WIDE dataset, which contains publicly available web images and their associated labels, to train the classifiers. These classifiers are used to detect the presence of concepts in each photo in a photo folder, and annotate the photos with suitable labels. To increase the accuracy of the annotation process, we conducted experiments on two simple but effective classification methods, k Nearest Neighbor (kNN) and Support Vector Machine (SVM), and determine the best method by considering their accuracy and speed. Bachelor of Engineering (Computer Science) 2011-06-06T07:37:10Z 2011-06-06T07:37:10Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/44873 en Nanyang Technological University 62 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::Information systems::Information storage and retrieval
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Luong, Phuoc Thanh.
Image annotation by search
description The recent advances in technology have led to an exponential growth in the number of digital images being stored on the Internet as well as in personal computers. Effective methods to organize and index photos based on semantic content have become essential to provide users with the convenience of searching their albums for specific content without prior manual annotation. However, querying for the image content is still a challenging task which has attracted much research effort. In this paper, we present a photo query framework based on prior annotation. When the user provides a text query (e.g. “water”), the framework performs a search within the annotation database and finds relevant photos. To accomplish this goal, we built a set of classifiers to annotate user photos in advance, and used these annotations for query. We leveraged the NUS-WIDE dataset, which contains publicly available web images and their associated labels, to train the classifiers. These classifiers are used to detect the presence of concepts in each photo in a photo folder, and annotate the photos with suitable labels. To increase the accuracy of the annotation process, we conducted experiments on two simple but effective classification methods, k Nearest Neighbor (kNN) and Support Vector Machine (SVM), and determine the best method by considering their accuracy and speed.
author2 Xu Dong
author_facet Xu Dong
Luong, Phuoc Thanh.
format Final Year Project
author Luong, Phuoc Thanh.
author_sort Luong, Phuoc Thanh.
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 2011
url http://hdl.handle.net/10356/44873
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