Improved Superpixel-Based Fast Fuzzy C-Means Clustering for Image Segmentation

Superpixel-based fast fuzzy C-means clustering (SFFCM) is an efficient method for color image segmentation. However, it is sensitive to noise and blur. Its superpixel method called multiscale morphological gradient reconstruction (MMGR) is time consuming. In this paper, we propose an improved SFFCM method (ISFFCM) which replaces the MMGR in SFFCM with fuzzy simple linear iterative clustering (Fuzzy SLIC). Fuzzy SLIC is faster and more robust than MMGR for most types of noise, including salt and pepper noise, Gaussian noise and multiplicative noise. It is also more robust to image blur. In the validation experiments, we tested ISFFCM and SFFCM on the Berkeley benchmark. The experiment results show that our method outperforms SFFCM under noise and blurring environments.

[1]  Caiming Zhang,et al.  Fast and robust image segmentation using an superpixel based FCM algorithm , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[2]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Sabine Süsstrunk,et al.  Superpixels and Polygons Using Simple Non-iterative Clustering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Vincent Lepetit,et al.  A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images , 2010, MICCAI.

[5]  Hong Qin,et al.  Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over $L_{1}$ -Manifold Hyper-Graph of Discriminative Image Regions , 2017, IEEE Transactions on Image Processing.

[6]  Xavier Cufí,et al.  Yet Another Survey on Image Segmentation: Region and Boundary Information Integration , 2002, ECCV.

[7]  Marina Meila,et al.  Comparing clusterings: an axiomatic view , 2005, ICML.

[8]  Hong Yan,et al.  Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering , 2020 .

[9]  Shuang Wang,et al.  Fuzzy Superpixels for Polarimetric SAR Images Classification , 2018, IEEE Transactions on Fuzzy Systems.

[10]  Yanning Zhang,et al.  Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation , 2019, IEEE Transactions on Fuzzy Systems.

[11]  Martial Hebert,et al.  Toward Objective Evaluation of Image Segmentation Algorithms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Marcelo Pereyra,et al.  Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation , 2015, IEEE Transactions on Image Processing.

[13]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  Asoke K. Nandi,et al.  Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering , 2018, IEEE Transactions on Fuzzy Systems.

[15]  Min Bai,et al.  Deep Watershed Transform for Instance Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Calvin C. Zhao.  Critical Review : Contour Detection and Hierarchical Image Segmentation , 2015 .