  Parsing human poses in images is fundamental in extracting critical visual
information for artificial intelligent agents. Our goal is to learn
self-contained body part representations from images, which we call visual
symbols, and their symbol-wise geometric contexts in this parsing process. Each
symbol is individually learned by categorizing visual features leveraged by
geometric information. In the categorization, we use Latent Support Vector
Machine followed by an efficient cross validation procedure to learn visual
symbols. Then, these symbols naturally define geometric contexts of body parts
in a fine granularity. When the structure of the compositional parts is a tree,
we derive an efficient approach to estimating human poses in images.
Experiments on two large datasets suggest our approach outperforms state of the
art methods.
