Abstract
In this paper, we propose a new image annotation method by combining content-based image annotation and tag-based image annotation techniques. Content-based image annotation technique is adopted to extract 'loosely defined concepts' by analyzing pre-given images' features such as color moment (CM), edge orientation histogram (EOH), and local binary pattern (LBP), followed by constructing a set of SVMs for 100 loosely defined concepts. A base-vector for each concept, similar to tag-based image annotation technique, is then constructed by using SVMs' predicted probabilistic results for sample-images whose main concepts are known. Finally cosine similarity between a query-image vector and the base vector is calculated for each concept. Experimental results show that our proposed method outperforms content-based image annotation technique by about 23% in accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 272-276 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479979783 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 - Marrakech, Morocco Duration: 2014 Nov 23 → 2014 Nov 27 |
Other
| Other | 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 |
|---|---|
| Country | Morocco |
| City | Marrakech |
| Period | 14/11/23 → 14/11/27 |
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Keywords
- image annotation
- support vector machine
- vector space model
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Signal Processing
- Computer Networks and Communications
Cite this
Image annotation fusing content-based and tag-based technique using support vector machine and vector space model. / Chan, Shan Bin; Yamana, Hayato; Le, Duy Dinh; Satoh, Shinichi.
Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 272-276 7081558.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Image annotation fusing content-based and tag-based technique using support vector machine and vector space model
AU - Chan, Shan Bin
AU - Yamana, Hayato
AU - Le, Duy Dinh
AU - Satoh, Shinichi
PY - 2014
Y1 - 2014
N2 - In this paper, we propose a new image annotation method by combining content-based image annotation and tag-based image annotation techniques. Content-based image annotation technique is adopted to extract 'loosely defined concepts' by analyzing pre-given images' features such as color moment (CM), edge orientation histogram (EOH), and local binary pattern (LBP), followed by constructing a set of SVMs for 100 loosely defined concepts. A base-vector for each concept, similar to tag-based image annotation technique, is then constructed by using SVMs' predicted probabilistic results for sample-images whose main concepts are known. Finally cosine similarity between a query-image vector and the base vector is calculated for each concept. Experimental results show that our proposed method outperforms content-based image annotation technique by about 23% in accuracy.
AB - In this paper, we propose a new image annotation method by combining content-based image annotation and tag-based image annotation techniques. Content-based image annotation technique is adopted to extract 'loosely defined concepts' by analyzing pre-given images' features such as color moment (CM), edge orientation histogram (EOH), and local binary pattern (LBP), followed by constructing a set of SVMs for 100 loosely defined concepts. A base-vector for each concept, similar to tag-based image annotation technique, is then constructed by using SVMs' predicted probabilistic results for sample-images whose main concepts are known. Finally cosine similarity between a query-image vector and the base vector is calculated for each concept. Experimental results show that our proposed method outperforms content-based image annotation technique by about 23% in accuracy.
KW - image annotation
KW - support vector machine
KW - vector space model
UR - http://www.scopus.com/inward/record.url?scp=84988227368&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988227368&partnerID=8YFLogxK
U2 - 10.1109/SITIS.2014.76
DO - 10.1109/SITIS.2014.76
M3 - Conference contribution
AN - SCOPUS:84988227368
SP - 272
EP - 276
BT - Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -