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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zeiler, M.D. Krishnan, D. Taylor, G.W. Fergus, R. |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept. of Computer Science, Courant Institute, New York University (Zeiler, M.D.; Krishnan, D.; Taylor, G.W.; Fergus, R.) |
| Abstract | Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images. |
| Starting Page | 2528 |
| Ending Page | 2535 |
| File Size | 2031275 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539957 |
| 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 | Convolution Decoding Image edge detection Image representation Filters Computer architecture Robustness Object recognition Feature extraction Image restoration |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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