How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?
2023 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 272, article id 110587Article in journal (Refereed) Published
Abstract [en]
Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.
Place, publisher, year, edition, pages
Elsevier B.V. , 2023. Vol. 272, article id 110587
Keywords [en]
Image processing, Image segmentation, Metaheuristic algorithms, Multi-level thresholding, Optimisation, Biomimetics, Optimization, Signal to noise ratio, Image thresholding, Images processing, Images segmentations, Meta-heuristics algorithms, Multilevel thresholding, Multilevels, Optimisations, Optimization problems, Similarity indices, Threshold-value
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-62583DOI: 10.1016/j.knosys.2023.110587ISI: 001004191400001Scopus ID: 2-s2.0-85158041478OAI: oai:DiVA.org:mdh-62583DiVA, id: diva2:1760578
2023-05-302023-05-302023-06-28Bibliographically approved