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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wolf, L. Bileschi, S. Meyers, E. |
| Copyright Year | 2006 |
| Description | Author affiliation: Massachusetts Institute of Technology (Wolf, L.) |
| Abstract | Flat appearance-based systems, which combine clever image representations with standard classifiers, might be the most effective way to recognize objects using current technologies. In the future, however, it seems probable that hierarchical representations might have better performance. In such systems, the image representation consists of a sequence of sets of features, where each subsequent set is computed based on the previous sets. The main contributions of this paper are to: (1) pose the question "what is the best way to employ discriminative methods for hierarchical image representations?"; (2) enumerate some of the alternative hierarchies while drawing connections to recent work by brain researchers; (3) study experimentally the different alternatives. As we will show, the strategy used can make a substantial difference. |
| Starting Page | 2153 |
| Ending Page | 2160 |
| File Size | 254585 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769525970 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2006.220 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine vision Image representation Computer vision Neurofeedback Biology computing Humans Computer architecture Image recognition Visual system Feedforward systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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