Presentation | 2002/11/15 Parallel Optimization of Class Configuration and Feature Space for Object Recognition Mihoko SHIMANO, Kenji NAGAO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper presents a new method to classify objects in images into categories explicitly specified by applications. In many object recognition methods, these categories define the classes for supervised classification themselves. In general, however, separability of such classes isn't guaranteed. A solution to this problem has been found that combines Fisher's separability criterion and information criterion of AIC to optimize the class configuration and the feature space, increasing the class-separability. Effectiveness of the new method will be demonstrated using real images of human faces. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | object recognition / Fisher's discirminant analysis / visual learning / AIC |
Paper # | PRMU2002-128 |
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Committee | PRMU |
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Conference Date | 2002/11/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Parallel Optimization of Class Configuration and Feature Space for Object Recognition |
Sub Title (in English) | |
Keyword(1) | object recognition |
Keyword(2) | Fisher's discirminant analysis |
Keyword(3) | visual learning |
Keyword(4) | AIC |
1st Author's Name | Mihoko SHIMANO |
1st Author's Affiliation | Matsushita. Electric Industrial Co., Ltd. Advanced Technology Research Laboratories() |
2nd Author's Name | Kenji NAGAO |
2nd Author's Affiliation | Matsushita. Electric Industrial Co., Ltd. Advanced Technology Research Laboratories |
Date | 2002/11/15 |
Paper # | PRMU2002-128 |
Volume (vol) | vol.102 |
Number (no) | 471 |
Page | pp.pp.- |
#Pages | 6 |
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