Abstract
With the advent of the multimedia era, the needs to get the image that an user wants from a lot of images is going to be more concerned about. In this paper, we propose an interactive image retrieval system which employs back-propagation neural networks using the words that represents the user's sensibility, in order to deal with the user's ambiguous queries. When an user inputs the words, this system sets the synapse of the network which represents both the user and the word and displays candidate images according to the output values of the neural network. The user evaluates the similarity to the image that he wants to get until the system displays the optimal images, and the system produces the set of teach signals according to the user's evaluation. After training the network, the system displays new candidate images. The inputs of the neural network are image features which has one-to-one correspondence with images in the databases. We implemented this system on Sun SPARC station, and could see that the system could improve the candidate images each time an user evaluate them.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE |
| Pages | 1563-1567 |
| Number of pages | 5 |
| Volume | 3 |
| Publication status | Published - 1997 |
| Event | Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4) - Houston, TX, USA Duration: 1997 Jun 9 → 1997 Jun 12 |
Other
| Other | Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4) |
|---|---|
| City | Houston, TX, USA |
| Period | 97/6/9 → 97/6/12 |
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ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Artificial Intelligence
Cite this
Image retrieval system capable of learning the user's sensibility using neural networks. / Kageyama, Yoshiteru; Saito, Hideo.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 3 IEEE, 1997. p. 1563-1567.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Image retrieval system capable of learning the user's sensibility using neural networks
AU - Kageyama, Yoshiteru
AU - Saito, Hideo
PY - 1997
Y1 - 1997
N2 - With the advent of the multimedia era, the needs to get the image that an user wants from a lot of images is going to be more concerned about. In this paper, we propose an interactive image retrieval system which employs back-propagation neural networks using the words that represents the user's sensibility, in order to deal with the user's ambiguous queries. When an user inputs the words, this system sets the synapse of the network which represents both the user and the word and displays candidate images according to the output values of the neural network. The user evaluates the similarity to the image that he wants to get until the system displays the optimal images, and the system produces the set of teach signals according to the user's evaluation. After training the network, the system displays new candidate images. The inputs of the neural network are image features which has one-to-one correspondence with images in the databases. We implemented this system on Sun SPARC station, and could see that the system could improve the candidate images each time an user evaluate them.
AB - With the advent of the multimedia era, the needs to get the image that an user wants from a lot of images is going to be more concerned about. In this paper, we propose an interactive image retrieval system which employs back-propagation neural networks using the words that represents the user's sensibility, in order to deal with the user's ambiguous queries. When an user inputs the words, this system sets the synapse of the network which represents both the user and the word and displays candidate images according to the output values of the neural network. The user evaluates the similarity to the image that he wants to get until the system displays the optimal images, and the system produces the set of teach signals according to the user's evaluation. After training the network, the system displays new candidate images. The inputs of the neural network are image features which has one-to-one correspondence with images in the databases. We implemented this system on Sun SPARC station, and could see that the system could improve the candidate images each time an user evaluate them.
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M3 - Conference contribution
AN - SCOPUS:0030702536
VL - 3
SP - 1563
EP - 1567
BT - IEEE International Conference on Neural Networks - Conference Proceedings
PB - IEEE
ER -