Jaccard Index in Ensemble Image Segmentation: An Approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Many methods have been applied to image segmentation, including unsupervised, supervised, and even deep learning-based models. Semantic and instance segmentation are the two most widely researched forms of segmentation. It is of value to use multiple methods to segment an image. In this paper, we present an image segmentation ensemble methodology. Multiple image segmentation methods are applied to an image and merged to create one segmentation using the proposed method. The technique uses the Jaccard index algorithm, sometimes called the Jaccard similarity coefficient and commonly known as Intersection over Union (IoU). This resulted in better segmentation results than the respective individual segmentation methods. This experiment was applied to mathematical expression recognition (MER), with the expressions taken from blackboards with varying degrees of noise, and lighting conditions, from different classroom environments. A summary of empirical results from the segmentation of multiple images is presented in the paper.

Original languageEnglish
Title of host publicationCIIS 2022 - 2022 5th International Conference on Computational Intelligence and Intelligent Systems
PublisherAssociation for Computing Machinery
Pages9-14
Number of pages6
ISBN (Electronic)9781450397612
DOIs
Publication statusPublished - 4 Nov 2022
Event5th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2022 - Quzhou, China
Duration: 4 Nov 20226 Nov 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2022
Country/TerritoryChina
CityQuzhou
Period4/11/226/11/22

Keywords

  • Image segmentation
  • Image segmentation ensembles. Connected components labelling
  • Jaccard index
  • Minimum spanning tree
  • Region-based convolutional neural network

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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