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  • Combining color and spatial image features for unsupervised image segmentation with mixture modelling and spectral clustering [Elektronski vir]
    Panić, Branislav ...
    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.
    Vir: Mathematics [Elektronski vir]. - ISSN 2227-7390 (Vol. 11, iss. 23, Nov. 2023, str. 1-22)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2023
    Jezik - angleški
    COBISS.SI-ID - 175149571

    Povezava(-e):

    https://www.mdpi.com/2227-7390/11/23/4800
    Repozitorij Univerze v Ljubljani – RUL
    DOI

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