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Image Fuzzy Segmentation Using Aggregated Distance Functions and Pixel Descriptors

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Artificial Intelligence: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 973))

Abstract

This paper is a review of recent research on image fuzzy segmentation using the fuzzy c-means clustering algorithm based on a distance function constructed by applying the aggregation function on the sequence of the initial distance functions and pixel descriptors. In image segmentation algorithms, distance functions compare pixels and represent a decision criterion for the classification of pixels into image segments. Determination of the segmentation criterion is based on the information fusion process, where the application of the appropriated aggregation function enables the adjustment of the segmentation criteria according to the intuitively expected decision. Initial distance functions represents the basic criteria which are relevant for segmentation, and applied aggregation function represent a model for this basic criteria fusion into one final decision criteria. With regards to a new distance function construction by applying aggregation functions, in this article we present relevant properties of the following aggregation functions: minimum, maximum, weighted arithmetic mean, generalized means, product of powers, weighted arithmetic mean of powers and OWA aggregation functions. Beside the pixel color or color components, other pixel descriptors are important for image segmentation and other image processing tasks. For experimental verification of the methodology used in image segmentation, the fuzzy c-means clustering algorithm is used.

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Notes

  1. 1.

    Some authors regard it as non-increasing.

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Acknowledgements

The first author was supported by the Science Fund of the Republic of Serbia, \(\#\)Grant No. 6524105, AI-ATLAS. The second and third author has been supported by the Ministry of Education, Science and Technological Development through the project no. 451-03-68/2020-14/200156: “Innovative scientific and artistic research from the FTS (activity) domain”.

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Authors and Affiliations

  1. Department for Postgraduate Studies, Singidunum University, 32 Danijelova St., 11000, Belgrade, Serbia

    Endre Pap

  2. Faculty of Technical Sciences, Department of Fundamentals Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Ljubo Nedović & Nebojša Ralević

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  1. Endre Pap
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  2. Ljubo Nedović
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  3. Nebojša Ralević
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Correspondence to Endre Pap .

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  1. Singidunum University, Belgrade, Serbia

    Endre Pap

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Pap, E., Nedović, L., Ralević, N. (2021). Image Fuzzy Segmentation Using Aggregated Distance Functions and Pixel Descriptors. In: Pap, E. (eds) Artificial Intelligence: Theory and Applications. Studies in Computational Intelligence, vol 973. Springer, Cham. https://doi.org/10.1007/978-3-030-72711-6_14

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