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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Learning of Graphical Models and Efficient Inference for Object Class Recognition
verfasst von : Martin Bergtholdt, Jörg H. Kappes, Christoph Schnörr
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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We focus on learning graphical models of object classes from arbitrary instances of objects. Large intra-class variability of object appearance is dealt with by combining statistical local part detection with relations between object parts in a probabilistic network. Inference for view-based object recognition is done either with
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-search employing a novel and dedicated admissible heuristic, or with Belief Propagation, depending on the network size.
Our approach is applicable to arbitrary object classes. We validate this for “faces” and for “articulated humans”. In the former case, our approach shows performance equal or superior to dedicated face recognition approaches. In the latter case, widely different poses and object appearances in front of cluttered backgrounds can be recognized.