Unsupervised learning of Probabilistic Grammar-Markov Models for object categories
We introduce a Probabilistic Grammar-Markov Model (PGMM) which couples probabilistic context free grammars and Markov Random Fields. These PGMMs are generative models defined over attributed features and are used to detect and classify objects in natural images. PGMMs are designed so that they can p...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 31(2009), 1 vom: 25. Jan., Seite 114-28
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| 1. Verfasser: |
Zhu, Long
(VerfasserIn) |
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| Weitere Verfasser: |
Chen, Yuanhao,
Yuille, Alan |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2009
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S. |
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