2004 Fiscal Year Final Research Report Summary
Discriminant analysis of spatial data based on fusion of Markov random fields and machine learning
| Project/Area Number |
15540123
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
|
|---|
| Research Institution | KYUSHU UNIVERSITY (2004)
Hiroshima University (2003) |
|---|
Principal Investigator |
NISHII Ryuei Kyushu University, Faculty of Mathematics, Professor, 大学院・数理学研究院, 教授 (40127684)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
ASANO Akira Hiroshima University, Faculty of Integrated Arts and Sciences, Associate Professor, 総合科学部, 助教授 (60243987)
IIKURA Yoshikazu Hirosaki University, Faculty of Science and Engineering, Professor, 理工学部, 教授 (30109897)
EGUCHI Shinto Kyushu University, Institute of Statistical Mathematics, Professor, 統計基礎研究系, 教授 (10168776)
SAKATA Toshio Kyushu University, Faculty of Design, Professor, 大学院・芸術工学研究院, 教授 (20117352)
TANAKA Shojiro Shimane University, Interdisciplinary, Faculty of Science and Engineering, Professor, 総合理工学部, 教授 (00197427)
|
|---|
| Project Period (FY) |
2003 – 2004
|
| Keywords | AdaBoost / Discriminant analysis / Exponential loss function / Image segmentation / Machine learning / Markov random fields |
|---|
| Research Abstract |
Aims of the research were as follows : (a)Improvement of multispectral image classification due to machine learning based on spatial distributions of categories, (b)Derivation of selection method of machine learning approaches and model selection of Markov random fields, (c)Comparison of the proposed and the ordinary methods through actual satellite images. Hence the aims were to derive a fused classification method based on statistics and machine learning in short. Nishii and Eguchi (2004) proposed Spatial AdaBoost, which achieved the aims.
Spatial AdaBoost is carried out in the following steps, (1)Obtain the posterior probabilities of the training data. (2)Calculate averages of log posteriors in neighborhoods of each pixels, and regard them as classification functions. (3)Tune the weights for the log posteriors by minimizing the exponential risk sequentially. (4)Classify test data by the convex combination of log posteriors.
The proposed method is examined through simulated and real data sets, and it is seen that the method is very fast and shows a similar performance to MRF-based classifier. The method sometimes gives a negative weight for the log posterior for some case because the exponential loss puts a huge penalty for outlying misclassified data. Hence, Spatial AdaBoost based on robust loss functions is under investigation, and we obtain a partial answer.
Further, we studied issues related to image analysis and machine learning, for example, robust loss functions and properties ; corrections of geometric and topographic effects ; morphological texture analysis ; and derivation of rotation invariant moments for character recognition.
|
Research Products
(23 results)
