期刊
DIGITAL SIGNAL PROCESSING
卷 76, 期 -, 页码 75-83出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2018.02.005
关键词
Image segmentation; Just noticeable difference color histogram; Interval type-2 fuzzy clustering; Pareto-nondominated sorting
资金
- National Natural Science Foundation of China [61571361, 61202153, 61671377, 41471280, 61102095]
- Science and Technology Plan in Shaanxi Province of China [2014KJXX-72]
- Fundamental Research Funds for the Central Universities [GK201503063]
- New Star Team of Xi'an University of Posts and Telecommunications
- IR/D grant from the National Science Foundation, USA
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. (C) 2018 Elsevier Inc. All rights reserved.
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