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Multi-Objective Convolutional Learning for Face Labeling

Title
Multi-Objective Convolutional Learning for Face Labeling
Author
Yang, Ming-hsuan
Keywords
Labeling; Face; Training; Testing; Hair; Image edge detection; Semantics
Issue Date
2015-06
Publisher
IEEE
Citation
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 3451-3459
Abstract
This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications.
URI
http://ieeexplore.ieee.org/document/7298967/?isnumber=7298593&arnumber=7298967&tag=1http://hdl.handle.net/20.500.11754/25610
ISBN
978-1-4673-6964-0
ISSN
1063-6919
DOI
10.1109/CVPR.2015.7298967
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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