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Efficient Robust Model Fitting for Multistructure Data Using Global Greedy Search
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DOI：10.1109/TCYB.2019.2900096.png)
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Abstract
En 中文
In this paper, a new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers. The proposed method is composed of three steps: first, a conventional greedy search strategy is employed to generate (initial) model hypotheses based on the sequential fit-and-remove procedure because of its computational efficiency. Second, to efficiently generate accurate model hypotheses close to the true models, a novel global greedy search strategy initially samples from the inliers of the obtained model hypotheses and samples subsequent data subsets from the whole input data. Third, mutual information theory is applied to fuse the model hypotheses of the same model instance. The conventional greedy search strategy is used to generate model hypotheses for the remaining model instances, if the number of retained model hypotheses is less than that of the true model instances after fusion. The second and the third steps are performed iteratively until an adequate solution is obtained. Experimental results demonstrate the effectiveness and efficiency of the proposed method for model fitting.
Keywords:
Data models
Computational modeling
Search problems
Sampling methods
Fuses
Computer vision
Mutual information
Computer vision
fusion strategy
global greedy search
multistructure data
robust model fitting
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