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Robust model estimation by using preference analysis and information theory principles

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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.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62172197, and Grant 61972187, in part by the Natural Science Foundation of Fujian Province under Grant 2020J01825, Grant 2020J02024 and Grant 2021J011017, in part by the Fuzhou Science and Technology Project under Grant 2022-R-001, and in part by the National Key R &D Program of China under Grant 2022YFC3302200.

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, 350121, China

    Taotao Lai & Zuoyong Li

  2. College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Weice Wang

  3. School of Software Engineering, Tongji University, Shanghai, 200092, China

    Yizhang Liu

  4. College of Information Science and Technology, Jinan University, Guangzhou, 510632, China

    Shuyuan Lin

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  1. Taotao Lai
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Correspondence to Taotao Lai.

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The authors declare no conflict of interest. The datasets generated during and/or analysed during the current study are available in https://cs.adelaide.edu.au/\(\sim \)ssl/adelaidermf and http://www.vision.jhu.edu/data/hopkins155/.

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Lai, T., Wang, W., Liu, Y. et al. Robust model estimation by using preference analysis and information theory principles. Appl Intell 53, 22363–22373 (2023). https://doi.org/10.1007/s10489-023-04697-z

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