Stochastic And-Or grammars compactly represent both compositionality
and reconfigurability and have been used to model different types of
data such as images and events. We present a unified formalization of
stochastic And-Or grammars that is agnostic to the type of the data
being modeled, and propose an unsupervised approach to learning the
structures as well as the parameters of such grammars. Starting from
a trivial initial grammar, our approach iteratively induces
compositions and reconfigurations in a unified manner and optimizes
the posterior probability of the grammar. In our empirical
evaluation, we applied our approach to learning event grammars and
image grammars and achieved comparable or better performance than
A grammar fragment is a set of grammar rules that are rooted at a new nonterminal node and specify how the new nonterminal node generates one or more configurations of existing nodes.
We propose to search for And-Or fragments in the algorithm. An And-Or fragment contains:
By learning with And-Or fragments, we induce compositions and reconfigurations in a unified manner that is more efficient and robust than learning with other types of grammar fragments. The posterior gain of adding an And-Or fragment can be efficiently computed based on a set of sufficient statistics.
Kewei Tu, Maria Pavlovskaia and Song-Chun Zhu, "Unsupervised Structure Learning of Stochastic And-Or Grammars". In Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, Nevada, USA, December 5-10, 2013. (Supplementary material)(Poster)
Download here: jAOG v0.1
The work is supported by grants from DARPA MSEE project FA 8650-11-1-7149, ONR MURI N00014-10-1-0933, NSF CNS 1028381, and NSF IIS 1018751.