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キーワード:
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要旨:
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
experiments illustrate the meaningfulness of the discovered parts and
demonstrate state-of-the-art performance of the approach.
