Open AccessRobust fuzzy local information and L p ‐norm distance‐based image segmentation method
Author(s) -
Li Fang,
Qin Jing
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0539
Subject(s) - outlier , artificial intelligence , pattern recognition (psychology) , cluster analysis , image segmentation , robustness (evolution) , fuzzy logic , mathematics , segmentation , norm (philosophy) , fuzzy clustering , segmentation based object categorization , pixel , computer vision , scale space segmentation , computer science , algorithm , biochemistry , chemistry , political science , law , gene
A variant of fuzzy c‐means (FCM) clustering algorithm for image segmentation is provided. Unlike the L 2 ‐norm distance in FCM, L p with p ∈ 0 , 1norm is used to measure the distance of the pixel intensity to its cluster centre in the energy functional. Moreover, local spatial information and colour information are incorporated into the model to enhance the robustness to noise and outliers. The proposed algorithm is called fuzzy local information L p (FLILp) clustering. To overcome the difficulty of finding cluster centres, L p ‐norm distance is approximated by weighted L 2 distance. The advantages of FLILp are: (i) it is strongly robust to noise and outliers, (ii) it is applied to the original image and (iii) it preserves image edges. Numerical examples and comparisons of image segmentation on both synthetic and real images illustrate the outstanding performance and robustness of the proposed method.