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2010
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Automatic Discovery of Meaningful Object Parts with Latent CRFs

12 years 1 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a balance between being flexible enough to model such variation and discriminative enough to detect objects in cluttered, real world scenes. Motivated by these challenges we propose a latent conditional random field (CRF) based on a flexible assembly of parts. By modeling part labels as hidden nodes and developing an EM algorithm for learning from class labels alone, this new approach enables the automatic discovery of semantically meaningful object part representations. To increase the flexibility and expressiveness of the model, we learn the pairwise structure of the underlying graphical model at the level of object part interactions. Efficient gradient-based techniques are used to estimate the structure of the domain of interest and carried forward to the multi-label or object part case. Our e...
Paul Schnitzspan, Stefan Roth, Bernt Schiele
Added 06 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Paul Schnitzspan, Stefan Roth, Bernt Schiele
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