Compact and Discriminative Descriptor Inference Using Multi-Cues
Feature descriptors around local interest points are widely used in human action recognition both for images and videos. However, each kind of descriptors describes the local characteristics around the reference point only from one cue. To enhance the descriptive and discriminative ability from mult...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 12 vom: 25. Dez., Seite 5114-26 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2015
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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: | Feature descriptors around local interest points are widely used in human action recognition both for images and videos. However, each kind of descriptors describes the local characteristics around the reference point only from one cue. To enhance the descriptive and discriminative ability from multiple cues, this paper proposes a descriptor learning framework to optimize the descriptors at the source by learning a projection from multiple descriptors' spaces to a new Euclidean space. In this space, multiple cues and characteristics of different descriptors are fused and complemented for each other. In order to make the new descriptor more discriminative, we learn the multi-cue projection by the minimization of the ratio of within-class scatter to between-class scatter, and therefore, the discriminative ability of the projected descriptor is enhanced. In the experiment, we evaluate our framework on the tasks of action recognition from still images and videos. Experimental results on two benchmark image and two benchmark video data sets demonstrate the effectiveness and better performance of our method |
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| Beschreibung: | Date Completed 03.02.2016 Date Revised 27.01.2016 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2015.2479917 |