On methods for merging mixture model components suitable for unsupervised image segmentation tasks

Panić, Branislav (author), Nagode, Marko (author), Klemenc, Jernej (author), Oman, Simon (author)

Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.

• Language: English
• Keywords: mixture models, parameter estimation, clustering, unsupervised image segmentation
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 22 str.
• Numbering: Vol. 10, iss. 22, art. 4301
• PID: 20.500.12556/RUL-142662
• UDC: 51:004
• ISSN on article: 2227-7390
• DOI: 10.3390/math10224301
• COBISS.SI-ID: 129898499
• Publication date in RUL: 18.11.2022

Record is a part of a journal

• Title: Mathematics
• Shortened title: Mathematics
• Publisher: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: mešani modeli, ocena parametrov, grozdenje, nenadzorovana segmentacija slik

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0182
• Name: Razvojna vrednotenja