Program chairs' introduction to the first international workshop on stochastic image grammars (SIG-09) in conjunction with IEEE CVPR 2009
Siniša Todorović, Song-Chun Zhu · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · 2009
Hierarchical models, semantic contexts, compositionality, taxonomy of visual categories, visual event ontology, stochastic graph matching, and bottom-up/top-down inference are popular research topics in computer vision and pattern recognition. They can be viewed as different aspects of stochastic image grammars. The virtue of image grammars lies in their expressive power to represent an exponentially large number of object and event configurations by using a relatively much smaller vocabulary, and a few compositional rules. In addition to objects and events, various semantic contexts can also be associated with all levels of hierarchical descriptions in grammars, facilitating rich image interpretations.