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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/44873 |
| _version_ | 1811687433075425280 |
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| author | Luong, Phuoc Thanh. |
| author2 | Xu Dong |
| author_facet | Xu Dong Luong, Phuoc Thanh. |
| author_sort | Luong, Phuoc Thanh. |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T05:16:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/44873 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:16:14Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval Luong, Phuoc Thanh. Image annotation by search |
| title | Image annotation by search |
| title_full | Image annotation by search |
| title_fullStr | Image annotation by search |
| title_full_unstemmed | Image annotation by search |
| title_short | Image annotation by search |
| title_sort | image annotation by search |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval |
| url | http://hdl.handle.net/10356/44873 |
| work_keys_str_mv | AT luongphuocthanh imageannotationbysearch |