2013 Fiscal Year Final Research Report
Adaptive Content-based Image Retrieval using Semi-Supervised Learning Method
| Project/Area Number |
22500152
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Perception information processing/Intelligent robotics
|
|---|
| Research Institution | Nagoya University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
NOBORU Ohnishi 名古屋大学, 情報科学研究科, 教授 (70185338)
|
|---|
| Project Period (FY) |
2010-10-20 – 2013-03-31
|
| Keywords | 画像情報処理 |
|---|
| Research Abstract |
Automatic image annotation is a hopeful sub-technique for image database retrieval. We have been constructing a generative model system for automatic image annotation using semi-supervised learning method. As it can be easily unstable for the higher dimensions, we must apply a dimensionality reduction method in advance. Generally, conventional supervised dimensionality reduction method (using labeled samples) suffers from the degenerate covariance matrix problem in the case of a small number of samples. On the other hand, unsupervised dimensionality reduction method (using unlabeled samples) can't recognize the differences among the categories properly. In this study, we propose a novel semi-supervised dimensionality reduction method using a small number of labeled samples and a large number of unlabeled samples. By the result of experiments, the classification rate of the proposed method was 5.1 points better than that of the unsupervised method.
|
