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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wolf, L. Hueihan Jhuang Hazan, T. |
| Copyright Year | 2007 |
| Description | Author affiliation: Tel Aviv Univ., Tel Aviv (Wolf, L.) |
| Abstract | Several authors have noticed that the common representation of images as vectors is sub-optimal. The process of vectorization eliminates spatial relations between some of the nearby image measurements and produces a vector of a dimension which is the product of the measurements' dimensions. It seems that images may be better represented when taking into account their structure as a 2D (or multi-D) array. Our work bears similarities to recent work such as 2DPCA or Coupled Subspace Analysis in that we treat images as 2D arrays. The main difference, however, is that unlike previous work which separated representation from the discriminative learning stage, we achieve both by the same method. Our framework, "low-rank separators ", studies the use of a separating hyperplane which are constrained to have the structure of low-rank matrices. We first prove that the low-rank constraint provides preferable generalization properties. We then define two "low-rank SVM problems" and propose algorithms to solve these. Finally, we provide supporting experimental evidence for the framework. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 311349 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424411793 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2007.383099 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Pixel Principal component analysis Matrix decomposition Computer science Image analysis Particle separators Multidimensional systems Algorithm design and analysis Concatenated codes |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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