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UCLA, USA
Thursday 19 February 2009
15.00
4 th Floor Seminar Room
Alexandra House, 17 Queen Square, London, WC1N 3AR
Recursive Compositional Models for Vision
Recursive Compositional Models (RCMs) are a class of probability models designed to detect, recognize, parse, and segment visual objects. They take into account the statistical and computational complexities of visual patterns. The key design principle is recursive compositionality. Visual patterns are represented by RCMs in a hierarchical form where complex structures are composed of more elementary structures. Probabilities are defined over these structures exploiting properties of the hierarchy (e.g. long range spatial relationships can be represented by local potentials). The compositional nature of this representation enables efficient learning and inference algorithms. Hence the overall architecture of RCMs provides a balance between statistical and computational complexity.