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• W2790750810 abstract "Although the interval type-2 fuzzy c-means clustering algorithm (IT2FCM) can well represent the uncertainty in data, there remain some problems to be solved: how to initialize cluster centers and how to determine fuzzifiers. In order to solve these issues of IT2FCM for color image segmentation, a pareto-based interval type-2 fuzzy c-means with multi-scale just noticeable difference color histogram (PIT2FC-MJND) is proposed in this paper. A multi-scale just noticeable difference (JND) color histogram is firstly constructed by using many distance thresholds and utilized to provide initial cluster centers. Then, a modified type-reduction and de-fuzzification mechanism on this multi-scale JND color histogram is designed for updating membership functions and cluster centers. Moreover, a pareto-based strategy for determining the combination of fuzzifiers is presented by using a global fuzzy compactness function and a fuzzy separation function which are based on the constructed multi-scale JND color histogram. The experimental results on real, Berkeley and Weizmann Images confirm the validity of the proposed approach." @default.
• W2790750810 created "2018-03-29" @default.
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• W2790750810 date "2018-05-01" @default.
• W2790750810 modified "2023-10-18" @default.
• W2790750810 title "Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation" @default.
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• W2790750810 doi "https://doi.org/10.1016/j.dsp.2018.02.005" @default.
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