Abstract
Image retrieval for multimedia databases is becoming very important as the increase of digital image resources and similar image retrieval is recognized as one of the most important issues in the image database field. In similar image retrieval, it is difficult to support individual variation among users because even when a same image is given as a query by different users, the required retrieval results may be different. In this paper, we propose a new similar image retrieval method. The most important feature of the method is that we use users' indications for similarities among images to deal with individual variation among users. In our method, a vector space is dynamically created for each user's own indications of similar images. The vector retrieval space is used to compute similarities dependent on individual user's intentions in the query. Images in the database are mapped dynamically onto the vector space according to the user's intentions. The components of the image vectors are different on the vector space. Similar images have larger component values on an axis than their component values on other axes. The retrieval processing is performed by selecting a subspace constructed by the axes that mostly reflect the user's intention. On the subspace, the similar images required by the user's query are obtained. As the experimental study, we utilize this method to implement an image retrieval system which is used for searching same images with different resolutions. Experimental results are shown for clarifying the effectiveness of this method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Eighth IASTED International Conference on Internet and Multimedia Systems and Applications |
| Editors | M.H. Hamza |
| Pages | 91-96 |
| Number of pages | 6 |
| Publication status | Published - 2004 |
| Event | Proceedings of the Eighth IASTED International Conference on Internet and Multimedia Systems and Applications - Kauai, HI, United States Duration: 2004 Aug 16 → 2004 Aug 18 |
Other
| Other | Proceedings of the Eighth IASTED International Conference on Internet and Multimedia Systems and Applications |
|---|---|
| Country | United States |
| City | Kauai, HI |
| Period | 04/8/16 → 04/8/18 |
Fingerprint
Keywords
- Fourier Transform
- Semantic information retrieval
- Similar image retrieval
- Vector space model
ASJC Scopus subject areas
- Engineering(all)
Cite this
Implementing similar image retrieval based on a semantic information retrieval system. / Chen, Xing; Kiyoki, Yasushi.
Proceedings of the Eighth IASTED International Conference on Internet and Multimedia Systems and Applications. ed. / M.H. Hamza. 2004. p. 91-96.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Implementing similar image retrieval based on a semantic information retrieval system
AU - Chen, Xing
AU - Kiyoki, Yasushi
PY - 2004
Y1 - 2004
N2 - Image retrieval for multimedia databases is becoming very important as the increase of digital image resources and similar image retrieval is recognized as one of the most important issues in the image database field. In similar image retrieval, it is difficult to support individual variation among users because even when a same image is given as a query by different users, the required retrieval results may be different. In this paper, we propose a new similar image retrieval method. The most important feature of the method is that we use users' indications for similarities among images to deal with individual variation among users. In our method, a vector space is dynamically created for each user's own indications of similar images. The vector retrieval space is used to compute similarities dependent on individual user's intentions in the query. Images in the database are mapped dynamically onto the vector space according to the user's intentions. The components of the image vectors are different on the vector space. Similar images have larger component values on an axis than their component values on other axes. The retrieval processing is performed by selecting a subspace constructed by the axes that mostly reflect the user's intention. On the subspace, the similar images required by the user's query are obtained. As the experimental study, we utilize this method to implement an image retrieval system which is used for searching same images with different resolutions. Experimental results are shown for clarifying the effectiveness of this method.
AB - Image retrieval for multimedia databases is becoming very important as the increase of digital image resources and similar image retrieval is recognized as one of the most important issues in the image database field. In similar image retrieval, it is difficult to support individual variation among users because even when a same image is given as a query by different users, the required retrieval results may be different. In this paper, we propose a new similar image retrieval method. The most important feature of the method is that we use users' indications for similarities among images to deal with individual variation among users. In our method, a vector space is dynamically created for each user's own indications of similar images. The vector retrieval space is used to compute similarities dependent on individual user's intentions in the query. Images in the database are mapped dynamically onto the vector space according to the user's intentions. The components of the image vectors are different on the vector space. Similar images have larger component values on an axis than their component values on other axes. The retrieval processing is performed by selecting a subspace constructed by the axes that mostly reflect the user's intention. On the subspace, the similar images required by the user's query are obtained. As the experimental study, we utilize this method to implement an image retrieval system which is used for searching same images with different resolutions. Experimental results are shown for clarifying the effectiveness of this method.
KW - Fourier Transform
KW - Semantic information retrieval
KW - Similar image retrieval
KW - Vector space model
UR - http://www.scopus.com/inward/record.url?scp=10444236505&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=10444236505&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0889864284
SP - 91
EP - 96
BT - Proceedings of the Eighth IASTED International Conference on Internet and Multimedia Systems and Applications
A2 - Hamza, M.H.
ER -