NDLI: Human parsing with a cascade of hierarchical poselet based pruners

Content Provider	IEEE Xplore Digital Library
Author	Duan Tran Yang Wang Forsyth, D.
Copyright Year	2014
Description	Author affiliation: Univ. of Illinois at Urbana Champaign, Urbana, IL, USA (Duan Tran; Forsyth, D.) \|\| Univ. of Manitoba, Winnipeg, MB, Canada (Yang Wang)
Abstract	We address the problem of human parsing using part-based models. In particular, we consider part-based models that exploit rich pairwise relationship between parts, e.g. the color symmetry between left/right limbs. This poses a computational challenge since the state space of each part is very large, and algorithmic tricks (e.g. the distance transform) cannot be applied to handle these types of pairwise relationships. We propose to prune the state space of each part using a cascade of pruners. These pruners can filter out 99.6% of the states per part to about 500 states per part, while keeping the ground-truth states in the pruned state most of the time. In the pruned space, we can afford to apply human parsing models with more complex pairwise relationships between parts, such as the color symmetry. We demonstrate our method on a challenging human parsing dataset.
Starting Page	1
Ending Page	6
File Size	395714
Page Count	6
File Format	PDF
ISBN	9781479947614
DOI	10.1109/ICME.2014.6890316
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Indexes Image color analysis Torso Vectors Transforms Head gesture analysis human pose estimation
Content Type	Text
Resource Type	Article

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