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https://hdl.handle.net/2440/137299
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Type: | Conference paper |
Title: | Maximum Consensus by Weighted Influences of Monotone Boolean Functions |
Author: | Zhang, E. Suter, D. Tennakoon, R. Chin, T.J. Bab-Hadiashar, A. Truong, G. Gilani, S.Z. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022, vol.2022-June, pp.8954-8962 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2022 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781665469463 |
ISSN: | 1063-6919 |
Conference Name: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2022 - 24 Jun 2022 : New Orleans, Louisiana) |
Statement of Responsibility: | Erchuan Zhang, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, Syed Zulqarnain Gilani |
Abstract: | Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure; and the distribution used for sampling to estimate the influence measure. This paper studies the concept of weighted influences for solving MaxCon. In particular, we study the Bernoulli measures. Theoretically, we prove the weighted influences, under this measure, of points belonging to larger structures are smaller than those of points belonging to smaller structures in general. We also consider another “natural” family of weighting strategies: sampling with uniform measure concentrated on a particular (Hamming) level of the cube. One can choose to have matching distributions: the same for defining the measure as for implementing the sampling. This has the advantage that the sampler is an unbiased estimator of the measure. Based on weighted sampling, we modify the algorithm of Tennakoon et al., and test on both synthetic and real datasets. We show some modest gains of Bernoulli sampling, and we illuminate some of the interactions between structure in data and weighted measures and weighted sampling. |
Keywords: | Computer vision theory; Vision + X; Vision applications and systems |
Rights: | ©2022 IEEE |
DOI: | 10.1109/CVPR52688.2022.00876 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200103448 http://purl.org/au-research/grants/arc/DP200101675 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding |
Appears in Collections: | Computer Science publications |
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