Distributional properties and estimation in spatial image clustering
type: Item
instance of: scholarly article
title: Distributional properties and estimation in spatial image clustering
zbMATH Open document ID: 1434.62115
DOI: 10.1214/19-EJS1626
author: Zijuan Chen
author: Suojin Wang
published in: Electronic Journal of Statistics
publication date: 2019-11-26T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1573009454
review text: In machine learning and pattern recognition theory the conventional clustering analysis is point-wise and deals with discrete data points by looking for their associations with specific but \textit{a priori} unknown groups. This papers considers cluster analysis from the perspective of image segmentation problem where spatial relations are important. The distribution of the size of an individual cluster is defined without defining a random variable to be the size of an individual cluster. The main results of this paper are the new methods to estimate the spatial distribution function because without a distribution function of the clusters, it is hard to deal the statistical properties of the data of image clustering and perform efficient statistical inferences. The theoretical results are illustrated by experiments on the real word dataset related to spatial patterning among savanna trees.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 7136619
zbMATH Keywords: distributional properties
zbMATH Keywords: image processing
zbMATH Keywords: spatial statistics
reviewed by: Denis Sidorov
MaRDI profile type: Publication
cites work: Random Fields and Geometry
cites work: Level sets of random fields and applications: specular points and wave crests
cites work: Q5653327
cites work: A New Approach to Cluster Analysis: the Clustering-Function-Based Method
cites work: Model-based clustering
cites work: An advancement in clustering via nonparametric density estimation
cites work: Fully adaptive density-based clustering
cites work: A general Rice formula, Palm measures, and horizontal-window conditioning for random fields
cites work: Estimating the number of peaks in a random field using the Hadwiger characteristic of excursion sets, with applications to medical images
OpenAlex ID: W2982756550
description: scientific article; zbMATH DE number 7136619