Image annotation fusing content-based and tag-based technique using support vector machine and vector space model

Shan Bin Chan; Hayato Yamana; Duy Dinh Le; Shinichi Satoh

doi:10.1109/SITIS.2014.76

Image annotation fusing content-based and tag-based technique using support vector machine and vector space model

Shan Bin Chan, Hayato Yamana, Duy Dinh Le, Shinichi Satoh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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	https://doi.org/10.1109/SITIS.2014.76
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/Territory	Morocco
City	Marrakech
Period	14/11/23 → 14/11/27

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

Access to Document

10.1109/SITIS.2014.76

Cite this

Chan, S. B., Yamana, H., Le, D. D., & Satoh, S. (2014). Image annotation fusing content-based and tag-based technique using support vector machine and vector space model. In Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 (pp. 272-276). [7081558] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SITIS.2014.76

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 et al.

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

Chan, SB, Yamana, H, Le, DD & Satoh, S 2014, Image annotation fusing content-based and tag-based technique using support vector machine and vector space model. in Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014., 7081558, Institute of Electrical and Electronics Engineers Inc., pp. 272-276, 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, Marrakech, Morocco, 14/11/23. https://doi.org/10.1109/SITIS.2014.76

Chan SB, Yamana H, Le DD, Satoh S. Image annotation fusing content-based and tag-based technique using support vector machine and vector space model. In 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 https://doi.org/10.1109/SITIS.2014.76

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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.

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Image annotation fusing content-based and tag-based technique using support vector machine and vector space model

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