Hessian optimal design for image retrieval

Authors:
Ke Lu;Jidong Zhao;Yue Wu
Affiliations:
School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China;School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China;School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China
Venue:
Pattern Recognition
Year:
2011

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Abstract

Recently there has been a considerable interest in active learning from the perspective of optimal experimental design (OED). OED selects the most informative samples to minimize the covariance matrix of the parameters, so that the expected prediction error of the parameters, as well as the model output, can be minimized. Most of the existing OED methods are based on either linear regression or Laplacian regularized least squares (LapRLS) models. Although LapRLS has shown a better performance than linear regression, it suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power. In this paper, we propose a novel active learning algorithm called Hessian optimal design (HOD). HOD is based on the second-order Hessian energy for semi-supervised regression which overcomes the drawbacks of Laplacian based methods. Specifically, HOD selects those samples which minimize the parameter covariance matrix of the Hessian regularized regression model. The experimental results on content-based image retrieval have demonstrated the effectiveness of our proposed approach.