Cite as:
Yuille A L, 2011, "Learning hierarchical models of shape" Perception 40 ECVP Abstract Supplement, page 2

Learning hierarchical models of shape

A L Yuille

This talk summarizes recent work on learning hierarchical models of shape. Objects are represented by recursive compositional models (RCMs) which are constructed from hierarchical dictionaries of more elementary RCMs. These dictionaries are learnt in an unsupervised manner using principles such as suspicious coincidences and competitive exclusion. Dictionary elements are analogous to receptive field structures found in the visual cortex. For multiple objects, we learn hierarchical dictionaries which encourage part-sharing (ie sharing dictionary elements between different objects). This gives an efficient representation of multiple objects while enabling efficient inference and learning, We describe how this work can be formalized in terms of a breadth first search through the space of models. We demonstrate results on benchmarked real images,

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