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Robust model estimation by using preference analysis and information theory principles

Robust model estimation by using preference analysis and information theory principles Robust model estimation aims to estimate the parameters of a given geometric model, and then separate the outliers and inliers belonging to different model instances into different groups based on the estimated parameters. Robust model estimation is a fundamental task in computer vision and artificial intelligence, and mainly contains two components: data sampling for generating hypotheses and model selection for segmenting data. Over the past decade, a number of guided data sampling algorithms and model selection algorithms have been proposed separately. This results in that the performance of the robust model estimation method is still unsatisfactory. In this paper, we first present a comprehensive study of the above algorithms, by analyzing and comparing them. Then, we propose an efficient and effective robust model estimation method by using preference analysis and information theory principles. Specifically, we first employ our previously proposed data sampling algorithm based on preference analysis to sample data subsets for generating promising hypotheses. Then, we build a discriminative sparse affinity matrix based on the generated hypotheses by using information theory principles. Finally, we segment data by conducting a spectral clustering on the discriminative affinity matrix. Experimental results on the AdelaideRMF and the Hopkins 155 datasets show that the proposed method achieves higher segmentation accuracies than several state-of-the-art model estimation methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Robust model estimation by using preference analysis and information theory principles

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References (35)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-023-04697-z
Publisher site
See Article on Publisher Site

Abstract

Robust model estimation aims to estimate the parameters of a given geometric model, and then separate the outliers and inliers belonging to different model instances into different groups based on the estimated parameters. Robust model estimation is a fundamental task in computer vision and artificial intelligence, and mainly contains two components: data sampling for generating hypotheses and model selection for segmenting data. Over the past decade, a number of guided data sampling algorithms and model selection algorithms have been proposed separately. This results in that the performance of the robust model estimation method is still unsatisfactory. In this paper, we first present a comprehensive study of the above algorithms, by analyzing and comparing them. Then, we propose an efficient and effective robust model estimation method by using preference analysis and information theory principles. Specifically, we first employ our previously proposed data sampling algorithm based on preference analysis to sample data subsets for generating promising hypotheses. Then, we build a discriminative sparse affinity matrix based on the generated hypotheses by using information theory principles. Finally, we segment data by conducting a spectral clustering on the discriminative affinity matrix. Experimental results on the AdelaideRMF and the Hopkins 155 datasets show that the proposed method achieves higher segmentation accuracies than several state-of-the-art model estimation methods.

Journal

Applied Intelligence – Springer Journals

Published: Oct 1, 2023

Keywords: Robust model estimation; Preference analysis; Information theory principles

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