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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nakayama, H. Harada, T. Kuniyoshi, Y. |
| Copyright Year | 2010 |
| Description | Author affiliation: Grad. School of Information Science and Technology, The University of Tokyo (Nakayama, H.; Harada, T.; Kuniyoshi, Y.) |
| Abstract | Local features provide powerful cues for generic image recognition. An image is represented by a “bag” of local features, which form a probabilistic distribution in the feature space. The problem is how to exploit the distributions efficiently. One of the most successful approaches is the bag-of-keypoints scheme, which can be interpreted as sparse sampling of high-level statistics, in the sense that it describes a complex structure of a local feature distribution using a relatively small number of parameters. In this paper, we propose the opposite approach, dense sampling of low-level statistics. A distribution is represented by a Gaussian in the entire feature space. We define some similarity measures of the distributions based on an information geometry framework and show how this conceptually simple approach can provide a satisfactory performance, comparable to the bag-of-keypoints for scene classification tasks. Furthermore, because our method and bag-of-keypoints illustrate different statistical points, we can further improve classification performance by using both of them in kernels. |
| Starting Page | 2336 |
| Ending Page | 2343 |
| File Size | 643876 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539921 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Layout Information geometry Statistical distributions Kernel Image sampling Space technology Image recognition Solids Linear approximation Information science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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