2002 Fiscal Year Final Research Report Summary
Rule Insertion and Extraction in Evolutionary Neural Networks for Image Retrieval
| Project/Area Number |
13680448
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | The University of Tokushima |
|---|
Principal Investigator |
FUKUMI Minoru The University of Tokushima Faculty of Engineering Associate Professor, 工学部, 助教授 (80199265)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
KITA Kenji The Univesity of Tokushima Center for Admanced Intormation Technology Professor, 高度情報化基盤センター, 教授 (10243734)
AKAMATSU Norio The Univesity of Tokushima Faculty of Engineering Professor, 工学部, 教授 (20035629)
MITSUKURA Yasue The Univesity of Tokushima Faculty of Engineering Instructor, 工学部, 助手 (60314845)
|
|---|
| Project Period (FY) |
2001 – 2002
|
| Keywords | Neural Network / Evolutionary Method / Rule Extraction / Image / Knowledge Extraction / Image Retrieval / Learning / Internet |
|---|
| Research Abstract |
This study presents a new knowledge incorporation and rule extraction method to deal with knowledge in neural networks for image retrieval in the Internet. The rule form of an if-then type can be inserted into a neural network (NN) as knowledge of a problem. NN is then trained by using a set of training samples. In this case the structure learning algorithm with forgetting is used to generate a small-sized NN system. After the NN training, rules are extracted from it. Furthemore evolutionary methods are used to train the neural network structure. The results of computer simulations for pattern recognition and chaos show that this approach can generate obvious network architectures and as a result simple rules compared with conventional rule extraction methods.
On the one hand, a method for image classification by neural networks which uses characteristic data extracted from images is studied. And also rule extraction from images has be done by neural network learning. Accuracy for image classification and key word extraction is about 70%.
In the future, these techniques must be unified and implemented for image retrieval.
|
