A fast fuzzy c-means algorithm for color image segmentation
- Hoel Le Capitaine, Carl Frélicot
- Corresponding Author
- Hoel Le Capitaine
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.9How to use a DOI?
- Color image segmentation is a fundamental task in many computer vision problems. A common approach is to use fuzzy iterative clustering algorithms that provide a partition of the pixels into a given number of clusters. However, most of these algorithms present several drawbacks: they are time consuming, and sensitive to initialization and noise. In this paper, we propose a new fuzzy c-means algorithm aiming at correcting such drawbacks. It relies on a new efficient cluster centers initialization and color quantization allowing faster and more accurate convergence such that it is suitable to segment very large color images. Thanks to color quantization and a new spatial regularization, the proposed algorithm is also more robust. Experiments on real images show the efficiency in terms of both accuracy and computation time of the proposed algorithm as compared to recent methods of the literature.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hoel Le Capitaine AU - Carl Frélicot PY - 2011/08 DA - 2011/08 TI - A fast fuzzy c-means algorithm for color image segmentation BT - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1074 EP - 1081 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.9 DO - https://doi.org/10.2991/eusflat.2011.9 ID - LeCapitaine2011/08 ER -