Locally-Parametric Pictorial Structures

Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and parameterization of the model is often restricted, for example, by assuming tree dependency structure and unimodal, data-independent pairwise interactions. These expressivity restrictions fail to capture important patterns in the data. On the other hand, local methods such as nearest-neighbor classification and kernel density estimation provide nonparametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose simi...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Benjamin Sapp, Chris Jordan, Ben Taskar
• Keywords: Computer Vision, CVPR 2010, Kernel Regression, Kernel Regression Estimates, Pictorial Structure