Symmetric subspace learning for image analysis
Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 12 vom: 21. Dez., Seite 5683-97 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the a priori knowledge that geometrical symmetry appears in several types of data, such as images, objects, faces, and so on. Experiments on artificial, facial expression recognition, face recognition, and object categorization databases highlight the superiority and the robustness of the proposed techniques, in comparison with standard SL techniques |
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| Beschreibung: | Date Completed 23.10.2015 Date Revised 02.02.2015 published: Print Citation Status MEDLINE |
| ISSN: | 1941-0042 |