In this paper, we propose a novel continuous latent semantic analysis fitting method, to efficiently and effectively estimate the parameters of model instances in data, based on latent semantic analysis and continuous preference analysis. Specifically, we construct a new latent semantic space (LSS): where inliers of different model instances are mapped into several independent directions, while gross outliers are distributed close to the origin of LSS. After that, we analyze the data distribution to effectively remove gross outliers in LSS, and propose an improved clustering algorithm to segment the remaining data points. On the one hand, the proposed fitting method is able to achieve excellent fitting results; due to the effective continuous preference analysis in LSS. On the other hand, the proposed method can efficiently obtain final fitting results due to the dimensionality reduction in LSS. Experimental results on both synthetic data and real images demonstrate that the proposed method achieves significant superiority over several state-of-the-art model fitting methods on both fitting accuracy and computational speed.

在本文中，我们提出了一种新颖的连续潜在语义分析拟合方法，以基于潜在语义分析和连续偏好分析，有效地估计数据中模型实例的参数。具体来说，我们构建了一个新的潜在语义空间（LSS）：将不同模型实例的离群值映射到几个独立的方向，而总离群值分布在靠近LSS起源的地方。之后，我们分析数据分布以有效去除LSS中的异常值，并提出一种改进的聚类算法以分割其余数据点。一方面，所提出的拟合方法能够达到很好的拟合效果；由于在LSS中进行了有效的连续偏好分析。另一方面，由于LSS的维数减少，因此该方法可以有效地获得最终的拟合结果。在合成数据和真实图像上的实验结果表明，与几种最新模型拟合方法相比，该方法在拟合精度和计算速度上均具有明显的优势。