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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ma, Songde Wang, Jinqiao Ouyang, Yi Tang, Ming Lu, Hanqing |
| Abstract | In this paper, a novel method for object categorization is proposed. We first analyze the phenomenon of large intra-class variation and attribute it to the "subcategory" problem. To reveal the local and distinct properties of the different subcategories, relative spaces are constructed. Then the weighted FLDs (Fisher Linear Discriminant) as weak learners trained in relative spaces are integrated with the boosting framework to form the final classifier. Experiments on 8 categories from Caltech database show the effectiveness of our algorithm. |
| Starting Page | 663 |
| Ending Page | 666 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781605583037 |
| DOI | 10.1145/1459359.1459454 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Relative space Geometric blur Object categorization Adaboost |
| Content Type | Text |
| Resource Type | Article |
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