An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

Keyi, Chen and Hangjun, Che and Leung, Man-Fai and Yadi, Wang (2022) An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks. In: 14th International Conference on Advanced Computational Intelligence, Wuhan, China. (Accepted)

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Abstract

Fuzzy c-means (FCM) has attracted wide attentions on picture segmentation as its fuzzy attribute matches the histogram distribution of a picture. However, the fuzzy c-means for the segmentation of a picture with massy noises is barely investigated. In this paper, an improved superpixel-based fuzzy c-means is proposed to segment a massy noise corrupted picture into more than two classes. Firstly, bilateral filtering is used to reduce the compact of noises and makes the picture smoother. Secondly an adaptive method is proposed to fuse the features of the original picture with filtered features. Thirdly simple linearly iterative clustering (SLIC) is used to detect the edge of the picture to avoid over-segmentation. Finally, the histogram-based fuzzy cmeans is used to get the segmentation result. In the experiments, the results show the proposed method achieves a 0.004 ∼ 0.014 higher mPA and 0.004 ∼ 0.06 higher mIoU than other seven algorithms. Besides the segmentation results also show that the over-segmentation is reduced.

Item Type: Conference or Workshop Item (Paper)
Keywords: Fuzzy C-Means, Massy Noise, Picture Segmentation
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 15 Jun 2022 10:57
Last Modified: 15 Jun 2022 11:36
URI: https://arro.anglia.ac.uk/id/eprint/707689

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