Fuzzy clustering with non-local information for image segmentation
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ORIGINAL ARTICLE
Fuzzy clustering with non-local information for image segmentation Jingjing Ma • Dayong Tian • Maoguo Gong Licheng Jiao
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Received: 1 July 2013 / Accepted: 1 January 2014 Springer-Verlag Berlin Heidelberg 2014
Abstract Fuzzy c-means (FCM) algorithms have been shown effective for image segmentation. A series of enhanced FCM algorithms incorporating spatial information have been developed for reducing the effect of noises. This paper presents a robust FCM algorithm with non-local spatial information for image segmentation, termed as NLFCM. It incorporates two factors: one is the local similarity measure depending on the differences between the central pixel and its neighboring pixels in the image; the other is the non-local similarity measure depended on all pixels whose neighborhood configurations are similar to their neighborhood pixels. Furthermore, an adaptive weight is introduced to control the trade-off between local similarity measure and non-local similarity measure. The experimental results on synthetic images and real images under different types of noises show that the new algorithm is effective, and they are relatively independent to the types of noises. Keywords Fuzzy clustering Image segmentation Non-local information Spatial information
1 Introduction Image segmentation is one of the key techniques in image understanding and computer vision. The task of the image segmentation is to divide an image into a number of nonoverlapping regions which have different characteristics such as gray level, color, tone, texture, etc. A lot of methods have been proposed for image segmentation, such
J. Ma D. Tian M. Gong (&) L. Jiao Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, No.2 South TaiBai Road, Mailbox 224, Xi’an 710071, China e-mail: [email protected]
as clustering [1–6], thresholding [7, 8], level set [9, 10], region growing [11, 12], and so on. Among the clustering methods, one of the most popular methods for image segmentation is fuzzy clustering which can retain more image information than hard clustering. Fuzzy c-means (FCM) [13] is one of the most widely used fuzzy clustering algorithms in image segmentation. Since the original FCM algorithm does not consider any local spatial information, it is very sensitive to noise. In order to overcome the above problem of FCM, many researchers [14–25] have brought local spatial information in the original FCM algorithm to improve its performance on image segmentation. Ahmed et al. [17] proposed FCM_S which modifies the objective function of FCM by a spatial neighborhood term. One drawback of FCM_S is that the spatial neighborhood term is calculated in each iteration, which is very time-consuming. To reduce the computational complexity of FCM_S, Chen and Zhang [18] proposed two variants, FCM_S1 and FCM_S2, which replace the neighborhood term of FCM_S by introducing the extra mean-filtered image and median-filtered image respectively. The mean-filtered image and median-filtered
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