Departmental Papers (CIS)

Document Type

Conference Paper

Date of this Version

6-1-2010

Comments

Sapp, B.; Jordan, C.; Taskar, B.; , "Adaptive pose priors for pictorial structures," Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on , vol., no., pp.422-429, 13-18 June 2010 doi: 10.1109/CVPR.2010.5540182

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Abstract

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 similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset [8]), while using only 15% of the training data as exemplars.

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Date Posted: 11 July 2012