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

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

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.

• Jezik: Angleški jezik
• Ključne besede: mixture models, parameter estimation, clustering, unsupervised image segmentation
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 22 str.
• Številčenje: Vol. 10, iss. 22, art. 4301
• PID: 20.500.12556/RUL-142662
• UDK: 51:004
• ISSN pri članku: 2227-7390
• DOI: 10.3390/math10224301
• COBISS.SI-ID: 129898499
• Datum objave v RUL: 18.11.2022

Gradivo je del revije

• Naslov: Mathematics
• Skrajšan naslov: Mathematics
• Založnik: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: mešani modeli, ocena parametrov, grozdenje, nenadzorovana segmentacija slik

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0182
• Naslov: Razvojna vrednotenja