Mixtures of trees for object recognition

By Sergey Ioffe, D.A. Forsyth. 2001

38 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Finding People in Images and Videos (2006, methodology): “limb classifiers built over 1st and 2nd order Gaussian filters in a dynamic programming framework similar to those of Felzenszwalb and Huttenlocher [2000] and Ioffe and Forsyth [2001b]1. Mikolajczyk et al. [2004] design features specifically tuned for human detection.”
Learning effective human pose estimation from inaccurate annotation (2011, methodology): “…further enhancements over the PSM: (i) we incorporate clustering of partial poses to account for images with poses containing occluded parts; (ii) we greatly improve both accuracy and computational efficiency by modeling part appearance using a state-of-theart mixture of linear classifiers [12].”
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation (2010, methodology): “Work related to ours has attempted to address these issues by using a mixture of trees [11] prior or a latent 'pose' variable [24].”
Analyzing 3D Objects in Cluttered Images (2012, methodology): “Learning structure and global mixtures: To simultaneously learn global mixture labels mn and edge structures associated with each mixture Em, we use an EM algorithm for learning mixtures of trees [20, 15].”
Hierarchical Gaussian process latent variable models (2007, background): “…The Gaussian process latent variable model (Lawrence, 2004; Lawrence, 2005) has proven to be a highly e.ective approach to probabilistic modelling of high dimensional data that lies on a non-linear manifold (Grochow et al., 2004; Urtasun et al., 2005; Urtasun et al., 2006; Ferris et al., 2007).”
Face detection, pose estimation, and landmark localization in the wild (2012, methodology): “While trees are natural for modeling human bodies [15, 40, 19], the natural tree structure for facial landmarks is not clear.”
Object Detection with Discriminatively Trained Part Based Models (2010, background): “For example, until recently speech recognition and machine translation systems based on n-gram language models outperformed systems based on grammars and phrase structure.”
Beyond trees: common-factor models for 2D human pose recovery (2005, methodology): “The cardboard people model of [7] uses a kinematic chain, and subsequent work by [4, 6, 9, 11] uses a full kinematic tree.”