Pictorial Structures for Object Recognition
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Improved human parsing with a full relational model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Detecting people using mutually consistent poselet activations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Learning effective human pose estimation from inaccurate annotation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning hierarchical poselets for human parsing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An efficient branch-and-bound algorithm for optimal human pose estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Articulated part-based model for joint object detection and pose estimation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Unsupervised discovery of mid-level discriminative patches
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Exploring the spatial hierarchy of mixture models for human pose estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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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 symbolwise 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. 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.