Combining color and spatial image features for unsupervised image segmentation with mixture modelling and spectral clustering

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

The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.

• Language: English
• Keywords: spectral clustering, mixture models, color features, spatial features, 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: 2023
• Number of pages: 22 str.
• Numbering: Vol. 11, iss. 23, art. 4800
• PID: 20.500.12556/RUL-152775
• UDC: 543.42
• ISSN on article: 2227-7390
• DOI: 10.3390/math11234800
• COBISS.SI-ID: 175149571
• Publication date in RUL: 06.12.2023

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: spektralno grozdenje, mešani modeli, barvne lastnosti, pozicijske lastnosti, segmentacija slik

Projects

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