swMATH ID: 43965
Software Authors: Ge, Shufei; Wang, Shijia; Elliott, Lloyd
Description: Shape modeling with spline partitions. Shape modelling (with methods that output shapes) is a new and important task in Bayesian nonparametrics and bioinformatics. In this work, we focus on Bayesian nonparametric methods for capturing shapes by partitioning a space using curves. In related work, the classical Mondrian process is used to partition spaces recursively with axis-aligned cuts, and is widely lied in multi-dimensional and relational data. The Mondrian process outputs hyper-rectangles. Recently, the random tessellation process was introduced as a generalization of the Mondrian process, partitioning a domain with non-axis aligned cuts in an arbitrary dimensional space, and outputting polytopes. Motivated by these processes, in this work, we propose a novel parallelized Bayesian nonparametric approach to partition a domain with curves, enabling complex data-shapes to be acquired. We apply our method to HIV-1-infected human macrophage image dataset, and also simulated datasets sets to illustrate our approach. We compare to support vector machines, random forests and state-of-the-art computer vision methods such as simple linear iterative clustering super pixel image segmentation. We develop an R package that is available at https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions.
Homepage: https://arxiv.org/abs/2108.02507
Source Code:  https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions
Dependencies: R
Keywords: Bayesian nonparametrics; Mondrian process; infinite relational model; BĂ©zier curve
Related Software: R
Cited in: 1 Publication

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1 Publication describing the Software, including 1 Publication in zbMATH Year
Shape modeling with spline partitions. Zbl 1499.62017
Ge, Shufei; Wang, Shijia; Elliott, Lloyd

Cited in 1 Serial

1 Statistics and Computing

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1 Statistics (62-XX)

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