Ranking with uncertain labels and its applications |
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Authors: | Yan Shuicheng Wang Huan Liu Jianzhuang Tang Xiaoou and Thomas S Huang |
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Affiliation: | (1) Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, USA;(2) Information Engineering Department, the Chinese University of Hong Kong, Hong Kong, China |
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Abstract: | The techniques for image analysis and classification generally consider the image sample labels fixed and without uncertainties.
The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the
case for the manual estimated image labels. A core ranking model is designed first as the bilinear fusing of multiple candidate
kernels. Then, the parameters for feature selection and kernel selection are learned simultaneously by maximum a posteriori
for given samples and uncertain labels. The provable convergency Expectation Maximization (EM) method is used for inferring
these parameters in an iterative manner. The effectiveness of the proposed algorithm is finally validated by the extensive
experiments on age ranking task and human tracking task. The popular FG-NET and the large scale Yamaha aging database are
used for the age estimation experiments, and our algorithm outperforms those state-of-the-art algorithms ever reported by
other interrelated literatures significantly. The experiment result of human tracking task also validates its advantage over
conventional linear regression algorithm.
A short version of this paper appeared in ICME07. |
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Keywords: | uncertain label age estimation human tracking |
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