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

Povzetek

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.

Ključne besede

mešani modeli;ocena parametrov;grozdenje;nenadzorovana segmentacija slik;mixture models;parameter estimation;clustering;unsupervised image segmentation;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 51:004
COBISS: 129898499 Povezava se bo odprla v novem oknu
ISSN: 2227-7390
Št. ogledov: 260
Št. prenosov: 47
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: mešani modeli;ocena parametrov;grozdenje;nenadzorovana segmentacija slik;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-22
Letnik: ǂVol. ǂ10
Zvezek: ǂiss. ǂ22
Čas izdaje: Nov. 2022
DOI: 10.3390/math10224301
ID: 17120297