Classification of digital images using topological signatures
Date Issued
2023-07
Author(s)
Sekuloski, Petar
Abstract
Topological Data Analysis (TDA) is a new area of
Applied Mathematics that has become increasingly popular in
recent years. TDA utilizes Persistent Homology, a mathematical
tool that analyzes the topology of data sets. The focus of this
paper is on using Persistent Homology to extract topological
signatures from digital images and investigate how these
signatures can improve image classification. In this short paper
there are some preliminary results obtained on real world
digital image datasets. There are improvement in evaluation
metrics from 19% to 37%, using topological signatures.
Applied Mathematics that has become increasingly popular in
recent years. TDA utilizes Persistent Homology, a mathematical
tool that analyzes the topology of data sets. The focus of this
paper is on using Persistent Homology to extract topological
signatures from digital images and investigate how these
signatures can improve image classification. In this short paper
there are some preliminary results obtained on real world
digital image datasets. There are improvement in evaluation
metrics from 19% to 37%, using topological signatures.
Subjects
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