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Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation / Hang Zhang in Pattern recognition, vol 121 (January 2022)  

Public [article]  inPattern recognition > vol 121 (January 2022) . - n° 108201 Titre : Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Haili Li, Auteur ; Ning Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification floue [Termes IGN] classification pixellaire [Termes IGN] filtre [Termes IGN] segmentation d'image [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application. Numéro de notice : A2022-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2021.108201 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99564 [article]