Abstract
An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.
| Original language | English |
|---|---|
| Pages (from-to) | 417-424 |
| Number of pages | 8 |
| Journal | International Journal of Neural Systems |
| Volume | 10 |
| Issue number | 5 |
| Publication status | Published - 2000 Oct |
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ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Content-based image retrieval system using neural networks. / Ikeda, T.; Hagiwara, Masafumi.
In: International Journal of Neural Systems, Vol. 10, No. 5, 10.2000, p. 417-424.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Content-based image retrieval system using neural networks.
AU - Ikeda, T.
AU - Hagiwara, Masafumi
PY - 2000/10
Y1 - 2000/10
N2 - An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.
AB - An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.
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M3 - Article
C2 - 11195939
AN - SCOPUS:0034303222
VL - 10
SP - 417
EP - 424
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
SN - 0129-0657
IS - 5
ER -