A unified hypothesis generation framework for multi-structure model fitting
A unified hypothesis generation framework for multi-structure model fitting
复制标题
用于多结构模型拟合的统一假设生成框架
DOI：
10.1016/j.neucom.2016.10.016
复制
发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Zhang Liming
中科院分区：
文献类型：
--
作者：
Lai Taotao;Wang Hanzi;Yan Yan;Zhang Liming
多结构模型拟合方法的成功与否，关键在于能否产生有希望的假设。然而，传统的多结构假设生成策略并没有利用模型选择结果的信息来指导后续的假设生成过程。这导致了这些假设生成策略在生成有希望的假设时通常计算成本很高的问题，特别是对于严重污染的多结构数据。为了解决这个问题，我们首先提出了一种引导采样策略，通过使用从模型选择结果中获得的信息来加速有希望的假设生成过程。然后，我们提出了一个统一假设生成（UHG）框架，该框架使用基于冷却计划的马尔可夫链蒙特卡罗过程，将传统的多结构假设生成策略与提出的引导采样策略有效地结合起来。在公共数据库上的实验结果表明，该方法在精度和效率方面明显优于几种最先进的采样方法，特别是在多结构数据上。
Generating promising hypotheses plays a critical role in the success of multi-structure model fitting methods. However, conventional multi-structure hypothesis generation strategies do not exploit the information derived from the results of model selection to guide the subsequent hypothesis generation process. This leads to the problem that these hypothesis generation strategies are often computationally expensive for generating promising hypotheses, especially for heavily contaminated multi-structure data. To address this problem, we first propose a guided sampling strategy to accelerate promising hypothesis generation process by using information derived from the results of model selection on the fly. Then we present a Unified Hypothesis Generation (UHG) framework, which effectively combines the conventional multi-structure hypothesis generation strategy with the proposed guided sampling strategy by using a Markov Chain Monte Carlo process based on a cooling schedule. Experimental results on public databases demonstrate that the proposed UHG achieves significant superiority over several state-of-the-art sampling methods in terms of accuracy and efficiency, especially on multi-structure data.