Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure
Nanjing University of Aeronautics and Astronautics · Institute of Automation · +1 more institution
Abstract
Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, to overcome the above problems, we first propose two variants, FCM_S1 and FCM_S2, of FCM_S to aim at simplifying its computation and then extend…
Citation impact
- FWCI
- 32.68
- Percentile
- 100%
- References
- 37
Authors
2Topics & keywords
- Outlier
- Pattern recognition (psychology)
- Cluster analysis
- Robustness (evolution)
- Artificial intelligence
- Euclidean distance
- Mathematics
- Computer science