A spatial minimum spanning tree filter

Jin, Yusheng; Zhao, Hong; Gu, Feifei; Bu, Penghui; Na, Mulun

doi:10.1088/1361-6501/abaa65

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…The direct ones consist of communication networks [137], transportation networks [138], water supply networks [139], and electrical grids [140]. Other practical cases based on MST include COVID-19 pandemic transmission forecasting [141], clustering [142], constructing trees for broadcasting [143], image registration and segmentation [144], circuit design [145] and emotion recognition [146]. There do exist several primary algorithms, namely, classic algorithm and faster algorithm; however, in consideration that such MST-like models will facilitate the development of multifarious industrial areas, more and more individuals have turned to design a variety of intuitive algorithms to figure out such issues, such as reinforcement learning [147], genetic algorithm [148] and fast parallel algorithm [149].…”

Section: Minimum Spanning Treementioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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Section: Minimum Spanning Treementioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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“…However, this method has little improvement in accuracy for low-texture regions. Jin et al [41] proposed a spatial MST filter. This filter increases paths for cost aggregation and uses recursive algorithms to speed up operation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Path Minimum Spanning Tree and Superpixel Based Cost Aggregation for Stereo Matching

Sun

2023

IEEE Access

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Cost aggregation is a key step in stereo matching algorithms. Despite more than a decade of development, most algorithms still encounter challenges such as high error rates in low-texture regions and blurred edges. To improve matching accuracy, we propose a novel cost aggregation method based on multipath minimum spanning tree (mPMST) and superpixel in this paper. The mPMST offers more optional paths for cost aggregation than the original MST by treating the reference image as an eight-connected graph. To improve both accuracy and computational efficiency, we innovatively run the mPMST for cost aggregation at the inside-superpixel level and superpixel level, which can obtain high accuracy in high-texture regions and low-texture regions respectively. In order to effectively fuse the two-level aggregated costs, we propose a novel adaptive weight based on calculating image entropy for each superpixel. This method can distinguish between regions with high and low texture and quantify texture complexity. Additionally, a novel disparity map refinement method is proposed to improve the quality of disparity maps using the novel cost aggregation structure proposed. In the experimental studies, we test our method on Middlebury and KITTI benchmarks. Average error rates of 5.94% for Middlebury 2006 and 24.51% for KITTI 2015 are achieved. Therefore, our method exhibits higher accuracy compared to other state-of-the-art approaches.

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“…For example, neurologists have used MSTs to compare brain networks, in which regions of the brain are nodes and edges denote structural or functional connections [4,5]. Computer scientists have used MSTs to segment video into meaningful partitions [6] and decompose images into a base layer and a detail layer [7]. Geographers have used MSTs to describe local building patterns [8].…”

Section: Introductionmentioning

confidence: 99%

Inferring the minimum spanning tree from a sample network

Larson,

Onnela

2021

Preprint

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Minimum spanning trees (MSTs) are used in a variety of fields, from computer science to geography.Infectious disease researchers have used them to infer the transmission pathway of certain pathogens.However, these are often the MSTs of sample networks, not population networks, and surprisingly little is known about what can be inferred about a population MST from a sample MST. We prove that if n nodes (the sample) are selected uniformly at random from a complete graph with N nodes and unique edge weights (the population), the probability that an edge is in the population graph's MST given that it is in the sample graph's MST is n N . We use simulation to investigate this conditional probability for G(N, p) graphs, Barabási-Albert (BA) graphs, graphs whose nodes are distributed in R 2 according to a bivariate standard normal distribution, and an empirical HIV genetic distance network. Broadly, results for the complete, G(N, p), and normal graphs are similar, and results for the BA and empirical HIV graphs are similar. We recommend that researchers use an edge-weighted random walk to sample nodes from the population so that they maximize the probability that an edge is in the population MST given that it is in the sample MST.

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A spatial minimum spanning tree filter

Cited by 5 publications

References 47 publications

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

Multi-Path Minimum Spanning Tree and Superpixel Based Cost Aggregation for Stereo Matching

Inferring the minimum spanning tree from a sample network

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