Robust visual data segmentation: Sampling from distribution of model parameters

Sadri, A, Tennakoon, R, Hoseinnezhad, R and Bab-Hadiashar, A 2018, 'Robust visual data segmentation: Sampling from distribution of model parameters', Computer Vision and Image Understanding, vol. 174, pp. 82-94.


Document type: Journal Article
Collection: Journal Articles

Title Robust visual data segmentation: Sampling from distribution of model parameters
Author(s) Sadri, A
Tennakoon, R
Hoseinnezhad, R
Bab-Hadiashar, A
Year 2018
Journal name Computer Vision and Image Understanding
Volume number 174
Start page 82
End page 94
Total pages 13
Publisher Academic Press
Abstract This paper approaches the problem of geometric multi-model fitting as a data segmentation problem. The proposed solution is based on a sequence of sampling hyperedges from a hypergraph, model selection and hypergraph clustering steps. We developed a sampling method that significantly facilitates solving the segmentation problem using a new form of the Markov-Chain-Monte-Carlo (MCMC) method to effectively sample from hyperedge distribution. To sample from this distribution effectively, our proposed Markov Chain includes new ways of long and short jumps to perform exploration and exploitation of all structures. To enhance the quality of samples, a greedy algorithm is used to exploit nearby structure based on the minimization of the Least kth Order Statistics cost function. Unlike common sampling methods, ours does not require any specific prior knowledge about the distribution of models. The output set of samples leads to a clustering solution by which the final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.
Subject Computer Vision
DOI - identifier 10.1016/j.cviu.2018.07.001
Copyright notice © 2018 Elsevier Inc
ISSN 1077-3142
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