Learning Visual Symbols for Parsing Human Poses in Images
This work addresses the challenge of extracting critical visual information for AI agents, with incremental improvements in pose estimation.
The paper tackles the problem of parsing human poses in images by learning self-contained visual symbols for body parts and their geometric contexts, resulting in an approach that outperforms state-of-the-art methods on two large datasets.
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.