Robust late fusion with rank minimization

In this paper, we propose a rank minimization method to fuse the predicted confidence scores of multiple models, each of which is obtained based on a certain kind of feature. Specifically, we convert each confidence score vector obtained from one model into a pairwise relationship matrix, in which each entry characterizes the comparative relationship of scores of two test samples. Our hypothesis is that the relative score relations are consistent among component models up to certain sparse deviations, despite the large variations that may exist in the absolute values of the raw scores. Then we formulate the score fusion problem as seeking a shared rank-2 pairwise relationship matrix based on which each original score matrix from individual model can be decomposed into the common rank-2 matrix and sparse deviation errors. A robust score vector is then extracted to fit the recovered low rank score relation matrix. We formulate the problem as a nuclear norm and 1 norm optimization ob...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Guangnan Ye, Dong Liu, I-Hong Jhuo, Shih-Fu Chang
• Keywords: Computer Vision, Cvpr 2012, Lagrange Multiplier, Relative Score, Score Matrix